U.S. patent application number 14/571785 was filed with the patent office on 2015-06-18 for sleep system for obtaining sleep state information.
The applicant listed for this patent is Blue Ocean Laboratories, Inc.. Invention is credited to Ronald Stuart BENSON, Ryan Cameron DENOMME.
Application Number | 20150164409 14/571785 |
Document ID | / |
Family ID | 53366930 |
Filed Date | 2015-06-18 |
United States Patent
Application |
20150164409 |
Kind Code |
A1 |
BENSON; Ronald Stuart ; et
al. |
June 18, 2015 |
SLEEP SYSTEM FOR OBTAINING SLEEP STATE INFORMATION
Abstract
Sleep systems having embedded sensors are described. In one
aspect, a sleep system includes a mattress and one or more force
sensors embedded within the mattress. The force sensors are
positioned within the mattress to sense movement of an occupant of
the mattress. The sleep system also includes one or more processors
coupled with the one or more force sensors. At least one of the
processors is configured to determine sleep state information for
the occupant based on data obtained from one or more of the force
sensors.
Inventors: |
BENSON; Ronald Stuart;
(Toronto, CA) ; DENOMME; Ryan Cameron; (Kitchener,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Blue Ocean Laboratories, Inc. |
North York |
|
CA |
|
|
Family ID: |
53366930 |
Appl. No.: |
14/571785 |
Filed: |
December 16, 2014 |
Current U.S.
Class: |
600/301 ;
600/483; 600/595 |
Current CPC
Class: |
A61B 5/4818 20130101;
A61M 2205/3569 20130101; A61B 5/742 20130101; A61M 2205/3584
20130101; A61M 2205/332 20130101; A61M 2205/3375 20130101; A61M
2230/62 20130101; Y02A 90/10 20180101; G16H 40/63 20180101; A61B
5/4806 20130101; A61B 5/7264 20130101; A61M 2021/0027 20130101;
A61M 2230/06 20130101; A47C 27/002 20130101; A61B 2562/0271
20130101; A61M 2205/13 20130101; A61B 5/0024 20130101; A61B 5/4266
20130101; G08B 21/0461 20130101; G16H 50/30 20180101; A61B 5/7278
20130101; A61M 2230/63 20130101; A61B 2562/0252 20130101; A61B 5/11
20130101; A61M 2205/52 20130101; A61M 2230/50 20130101; A61M
2021/0022 20130101; A61B 5/0816 20130101; A61B 5/4809 20130101;
G08B 6/00 20130101; A61B 2562/029 20130101; A61M 2205/3553
20130101; A61M 2230/42 20130101; G16H 40/67 20180101; A47C 27/15
20130101; A61B 5/7445 20130101; A61B 2562/0204 20130101; A61M 21/00
20130101; A61B 5/113 20130101; A61B 7/003 20130101; A61M 2021/0044
20130101; A61M 2205/3592 20130101; A47C 31/00 20130101; A61B
5/02405 20130101; A61B 5/02444 20130101; A61M 2205/505 20130101;
A61M 2205/8206 20130101; A61M 2205/3306 20130101; A61B 5/4812
20130101; A61B 5/746 20130101; A61M 2205/18 20130101; A61B 5/1116
20130101; A61B 2562/0247 20130101; A61B 2560/0242 20130101; A61M
2021/0083 20130101; A61B 5/1115 20130101; A61B 5/024 20130101; A61B
5/6892 20130101; A61G 7/05 20130101; A61B 2562/0219 20130101; A61B
5/4815 20130101; A61B 5/01 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/11 20060101 A61B005/11; A61B 7/00 20060101
A61B007/00; A61B 5/08 20060101 A61B005/08; A61B 5/01 20060101
A61B005/01; A61G 7/05 20060101 A61G007/05; A61B 5/024 20060101
A61B005/024 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 16, 2013 |
CA |
2836431 |
Claims
1. A sleep system comprising: a mattress; one or more force sensors
embedded within the mattress, the force sensors being positioned
within the mattress to sense movement of an occupant of the
mattress; and one or more processors coupled with the one or more
force sensors, at least one of the processors being configured to
determine sleep state information for the occupant based on data
obtained from one or more of the force sensors.
2. The sleep system of claim 1, wherein determining sleep state
information for the occupant comprises determining that the
occupant is asleep.
3. The sleep system of claim 1, wherein determining sleep state
information for the occupant comprises determining the sleep stage
of the occupant.
4. The sleep system of claim 3, wherein determining the sleep stage
of the occupant comprises: identifying movements of the occupant
from the data obtained from the one or more of the force sensors;
determining an amount of movements of an occupant within an epoch
of a predetermined duration; and determining the sleep stage by
comparing the amount of movements of the occupant within the epoch
to one or more predetermined thresholds.
5. The sleep system of claim 3, wherein the one or more processors
are further configured to trigger an alarm based on the sleep stage
of the occupant.
6. The sleep system of claim 1, wherein the processor is configured
to determine, from data obtained from one or more of the force
sensors, a heart rate of the occupant, and wherein the sleep state
information is determined based on the heart rate.
7. The sleep system of claim 1, wherein the processor is configured
to determine, from data obtained from one or more of the force
sensors, a respiration rate of the occupant, and wherein the sleep
state information is determined based on the respiration rate.
8. The sleep system of claim 1, further comprising a temperature
sensor embedded within the mattress, the temperature sensor being
positioned within the mattress to obtain temperature readings
associated with the occupant of the mattress, and wherein the
processor is configured to determine the sleep state information
based on the temperature readings.
9. The sleep system of claim 1, wherein determining sleep state
information for the occupant comprises determining that the
occupant is in a rapid eye movement sleep state.
10. The sleep system of claim 1, wherein determining sleep state
information for the occupant comprises determining that the
occupant is in a non-rapid eye movement sleep state.
11. The sleep system of claim 1, wherein the processor is further
configured to determine sleep onset latency based on the data
obtained from one or more of the force sensors.
12. The sleep system of claim 1, further comprising: an output
interface for providing feedback based on the sleep state
information.
13. The sleep system of claim 12, wherein the processor is further
configured to detect one or more sleep disorders and to trigger an
alert via the output interface when one or more of the sleep
disorders are detected.
14. The sleep system of claim 13, wherein the one or more sleep
disorders include insomnia.
15. The sleep system of claim 14, wherein the processor is further
configured to determine sleep onset latency based on the data
obtained from one or more of the force sensors and wherein the
processor is configured to detect insomnia based on the sleep onset
latency.
16. The sleep system of claim 13, wherein the one or more sleep
disorders include narcolepsy.
17. The sleep system of claim 16, wherein the processor is further
configured to determine sleep onset latency based on the data
obtained from one or more of the force sensors and wherein the
processor is configured to detect narcolepsy based on the sleep
onset latency.
18. The sleep system of claim 13, wherein the one or more sleep
disorders include sleep apnea.
19. The sleep system of claim 18, further comprising a microphone
coupled to the processor and wherein the processor is configured to
detect sleep apnea by: determining a sleep stage of the occupant;
and detecting sleep apnea based on the sleep stage of the occupant
and an audio signal generated by the microphone.
20. The sleep system of claim 13, wherein the one or more sleep
disorders include delayed sleep phase syndrome.
21. The sleep system of claim 20, wherein the processor is further
configured to determine sleep onset latency based on the data
obtained from one or more of the force sensors and wherein the
processor is further configured to detect delayed sleep phase
syndrome if the sleep onset latency exceeds a predetermined
threshold for at least a predetermined number of sleeps.
22. The sleep system of claim 13, wherein the one or more sleep
disorders include advanced sleep phase syndrome disorder.
23. The sleep system of claim 22, further comprising a clock
associated with the processor, the clock tracking a current time of
day, and wherein the processor is configured to detect advanced
sleep phase syndrome disorder by: detecting that the occupant has
gone to bed based on the data from the one or more force sensors;
determining that the occupant went to bed early by comparing the
time when the occupant went to bed to a predetermined time
threshold; detecting that the occupant has woken up based on the
data from the one or more force sensors; and determining that the
occupant has woken up early by comparing the current time when the
occupant got up from bed to another predetermined time threshold;
and in response to determining that the occupant has went to bed
early and woken up early, incrementing a counter which tracks the
number of days that the occupant has went to bed early and gotten
up early; determining that the occupant may have advanced sleep
phase syndrome disorder by comparing the counter to a predetermined
count threshold.
24. The sleep system of claim 23, wherein the processor is further
configured to: determine an awake latency based on the data from
the one or more force sensors, the awake latency representing the
elapsed time between when an occupant woke up and when they got up
from the mattress, and wherein the determination that the occupant
may have advanced sleep phase disorder is based on the awake
latency.
25. The sleep system of claim 13, wherein at least one of the force
sensors is located in a leg region of the mattress, the leg region
of the mattress being a region associated with an occupant's legs,
and wherein the one or more sleep disorders include periodic limb
movement disorder, and wherein the processor is configured to
determine a measure of leg movement based on the data from the one
or more force sensors that are located in the leg region and to
detect periodic limb movement disorder based on the measure of leg
movement.
26. The sleep system of claim 13, wherein the one or more sleep
disorders include sleep walking.
27. The sleep system of claim 1, further comprising a memory
associated with the processor, the memory storing characteristic
information associated with a plurality of predetermined sleep
positions and wherein the processor is configured to determine, by
comparing the data obtained from the force sensors to the
characteristic information, a sleep position associated with the
occupant.
28. The sleep system of claim 1, further comprising: a
machine-readable code affixed to the mattress, the machine-readable
code uniquely identifying the mattress from other mattresses, and
wherein the machine-readable code is readable by a mobile device to
associate the mobile device with the mattress.
29. The sleep system of claim 1, wherein at least some of the force
sensors are located in a left portion of the mattress and at least
some of the force sensors are located in a right portion of the
mattress, the left portion of the mattress being associated with a
first occupant and the right portion of the mattress being
associated with a second occupant, and wherein the processor is
configured to analyze data from the force sensors located in the
right portion of the mattress separately from data from the force
sensors located in the left portion of the mattress, the sleep
system further comprising: a first machine-readable code affixed to
the mattress and a second machine-readable code affixed to the
mattress, the first machine-readable code and the second
machine-readable code uniquely identifying the mattress from other
mattresses, the first machine-readable code and the second machine
readable code further identifying separate portions of the
mattress, and wherein the machine-readable codes are readable by a
mobile device to associate the mobile device with the mattress and
with one of the portions of the mattress.
30. The sleep system of claim 1, further comprising: conductive
thread, the conductive thread being sewn into a layer of the
mattress, the conductive thread providing a connection between the
force sensors and the processor, the conductive thread being
composed of a material which conducts an electrical signal.
31. The sleep system of claim 30, wherein the conductive thread is
sewn into a ticking layer or sock layer of the mattress.
Description
[0001] This application claims priority to Canadian Patent
Application Number 2,836,431, the contents of which are hereby
incorporated by reference.
TECHNICAL FIELD
[0002] The present disclosure relates to mattresses and, more
particularly, to an intelligent sleep system.
BACKGROUND
[0003] Applications have been developed which monitor a user's
sleep state. Such applications often operate on mobile devices,
such as smartphones, and often require the user to place their
mobile device on a mattress in order for the sleep state to be
monitored. Such applications rely on an accelerometer of the mobile
device for sleep state detection.
[0004] Such applications are generally limited in their
functionality and convenience. More particularly, a user must
remember to set their mobile device on their mattress or the
application will not track their sleep state and must ensure that
the mobile device is placed at a particular location of the
mattress or the application will not track their sleep state.
[0005] Additionally, the hardware provided on a mobile device only
allows limited information to be obtained and may suffer from
accuracy issues.
[0006] Thus there exists a need for methods and systems for
monitoring sleep state.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a top view of a sleep system in accordance with
example embodiments of the present disclosure;
[0008] FIG. 2 is a cross section of the sleep system of FIG. 1
taken along line 2-2 of FIG. 1;
[0009] FIG. 3 is a block diagram of an example sleep system;
[0010] FIG. 4 is a flowchart of a method of obtaining movement
information;
[0011] FIG. 5 is a flowchart of a method of determining sleep state
information, such as a sleep stage;
[0012] FIG. 6 is a flowchart of a method of determining heart
rate;
[0013] FIG. 7 is a flowchart of a method of determining respiration
rate;
[0014] FIG. 8 is a flowchart of a method of determining sleep
position;
[0015] FIG. 9 is a flowchart of a method of detecting a sleep
disorder;
[0016] FIG. 10 is a flowchart of a method of triggering an alert
based on mattress health information;
[0017] FIG. 11 is a flowchart of a method of determining sleep
environment information; and
[0018] FIG. 12 is a block diagram of a mobile device in accordance
with example embodiments of the present disclosure;
[0019] FIG. 13 is a block diagram of a server in accordance with
example embodiments of the present disclosure;
[0020] FIG. 14 is an example flowchart of a method for generating a
display screen in accordance with example embodiments of the
present disclosure;
[0021] FIG. 15 is an example display screen;
[0022] FIG. 16 is an example display screen;
[0023] FIG. 17 is an example display screen;
[0024] FIG. 18 is an example display screen;
[0025] FIG. 19 is an example display screen;
[0026] FIG. 20 is an example display screen; and
[0027] FIG. 21 is an example display screen.
[0028] Like reference numerals are used in the drawings to denote
like elements and features.
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
[0029] In one aspect, the present disclosure describes a sleep
system. The sleep system includes a mattress and one or more force
sensors embedded within the mattress. The force sensors are
positioned within the mattress to sense movement of an occupant of
the mattress. The sleep system also includes one or more processors
coupled with the one or more force sensors. At least one of the
processors is configured to determine sleep state information for
the occupant based on data obtained from one or more of the force
sensors.
[0030] In another aspect, a sleep system is described which
includes a mattress and one or more sensors embedded within the
mattress. The sleep system also includes an output interface and
one or more processors coupled with the one or more sensors and the
output interface. At least one of the processors is configured to
determine mattress health information based on data obtained from
one or more of the sensors and to generate an alert via the output
interface based on the mattress health information. The sleep
system also includes a memory coupled with the at least one
processor.
[0031] In yet another aspect, a sleep system is described which
includes one or more sensors embedded within a mattress. The sleep
system also includes one or more sensors provided in a peripheral
that is external to the mattress. The sleep system further includes
an output interface and one or more processors receiving data from
the one or more sensors embedded within the mattress and the one or
more sensors provided in the peripheral. At least one of the
processors is configured to determine sleep environment information
based on the data from one or more of the sensors embedded within
the mattress and the data from the one or more sensors provided in
the peripheral. At least one of the processors is configured to
generate an output on the output interface based on the sleep
environment information.
[0032] In yet another aspect, a mobile device is described. The
mobile device includes a communication subsystem and a processor
coupled with the communication subsystem. The mobile device further
includes a memory coupled with the processor. The memory is
configured to receive data from a sleep system that includes one or
more embedded sensors via the communication subsystem and to
generate one or more display screens based on the received
data.
[0033] In yet another aspect, a server is described. The server
includes a communication subsystem and a processor coupled with the
communication subsystem. The server further includes a memory
coupled with the processor. The memory is configured to receive
data from a sleep system that includes one or more embedded sensors
via the communication subsystem and to generate one or more display
screens based on the received data.
[0034] Other example embodiments of the present disclosure will be
apparent to those of ordinary skill in the art from a review of the
following detailed description in conjunction with the
drawings.
[0035] Referring now to FIG. 1, a top view of an example sleep
system 100 in accordance with example embodiments of the present
disclosure is illustrated. The sleep system 100 may, in at least
some embodiments, be referred to as a smart mattress, an
intelligent mattress, a sleep information tracking assembly or in
some cases as simply a mattress.
[0036] The sleep system 100 includes a mattress 101. The mattress
101 provides support for an occupant while sleeping. The mattress
101 may, for example, be sized according to any one of a plurality
of traditional mattress sizes. For example, in various embodiments
the mattress 101 may be sized according to one of the following
standard sizes: crib/toddler, single, twin, double, full, queen,
king, wide double, Olympic queen, queen, king, super king,
California king, or king long. Dimensions associated with these
mattresses are readily available and will not be listed
exhaustively herein. However, by way of example, in an embodiment
in which the mattress is a queen sized mattress, it may have a
height of 80 inches and a width of 60 inches.
[0037] The mattress 101 may be of other sizes apart from those
listed above. For example, custom mattress sizes may be used in
some embodiments.
[0038] The mattress 101 is generally a large pad for supporting a
reclining body (the reclining body is generally referred to herein
as the occupant). The interior of the mattress may be constructed
of, for example, an absorbing layer such as foam and may, in some
embodiments, be constructed of a coil.
Occupant Monitoring Sensors
[0039] In addition to the mattress, the sleep system 100 includes
one or more sensors which may be used for monitoring an occupant of
the mattress. These occupant monitoring sensors may include force
sensors 120a-120h, which may be used to detect movement and
positioning of an occupant, a body temperature sensor 122 used to
detect a body temperature of an occupant, and/or a humidity sensor
124 used to detect humidity associated with an occupant. These
sensors will be discussed in greater detail below.
Force Sensors
[0040] As noted above, the sleep system 100 may include one or more
force sensors 120a-120h. The force sensors 120a-120h are embedded
within the mattress 101 in at least some embodiments and are,
therefore, illustrated with broken lines in the top view of FIG. 1.
In at least some embodiments, one or more of the force sensors
120a-120h are positioned within the mattress to sense movement of
an occupant of the mattress.
[0041] To facilitate understanding of the layout of the force
sensor 120a-120h with respect to the mattress 101, the various
sides of the mattress have been labelled in FIG. 1. More
specifically, a top side 102 is the side of the mattress 101 which
is generally nearest an occupant's head and which may also be
nearest a headboard (not shown) of a bed on which the mattress
rests. A bottom side 104 is opposite the top side 102 and is
generally nearest an occupant's feet and which may also be nearest
a footboard (not shown) of the bed.
[0042] The top side 102 and the bottom side are connected by two
generally parallel sides which may be referred to as a left side
106 and a right side 108. It will be appreciated that the
orientations of sides referred to above describe the mattress in
one possible position and these orientations may change, for
example, if the mattress is flipped or rotated.
[0043] In the example illustrated, the mattress is sized for
concurrent use by two occupants (i.e. it is a two-person mattress).
For example, the mattress 101 may be a queen, king, wide double,
Olympic queen, queen, king, super king, California king, king long,
or in some cases a full or double sized mattress. In such
embodiments, a center line 110 may be defined which is located
equidistant from the left side 106 and the right side 108 and which
bisects the mattress 101 to divide it into two equal parts, which
may generally be referred to as a left portion 112 and a right
portion 114. Each of these portions may be associated with a
separate sensor set 150, 152. That is, each of the left portion 112
and the right portion 114 may be associated with a separate set
150, 152 of sensors such as a separate set of force sensors
120a-120h.
[0044] Accordingly, in the example illustrated, there are two sets
of sensors--a first set 150 is located on the left portion 112 of
the mattress 101 to obtain data from a first occupant, who sleeps
on the left portion 112 of the mattress 101, generally near the
left side 106. Similarly, a second set 152 is located on the right
portion 114 of the mattress 101 to obtain data from a second
occupant, who sleeps on the right portion 114 of the mattress 101,
generally near the right side 108.
[0045] It will be appreciated that, in other embodiments, there may
be other sets of sensors included in the mattress instead of or in
addition to the first set 150 and the second set 152 of sensors
illustrated in FIG. 1. For example, in some embodiments, the
mattress may be sized for single occupancy. By way of example, in
some such embodiments the mattress may be a twin mattress which may
be occupied by a single person. In such embodiments, the mattress
may be equipped with a single set of sensors. Further, in other
embodiments, the mattress 101 may be equipped with more than two
sets of sensors. For example, the embodiment of FIG. 1 could
additionally include a third set of sensors which may, for example,
be disposed in the middle of the mattress. For example, the third
set could be symmetric across the center line 110. This third set
could, for example, be used to obtain data associated with an
occupant when the mattress (which is large enough to be occupied by
two people), is only occupied by a single person who generally
sleeps in the center of the bed.
[0046] Each sensor set 150, 152 is used for obtaining data
associated with a single occupant and, in the example illustrated,
each sensor set 150, 152 includes a plurality of force sensors
120a-120b. The force sensors 120a-120b include one or more force
sensors 120a, 120b, 120c that are generally oriented near an upper
body of an occupant. These force sensors 120a, 120b, 120c may be
referred to as upper body force sensors. These force sensors 120a,
120b, 120c are oriented to capture data in the vicinity of an
occupant's head, shoulder, and/or chest region. These force sensors
120a, 120b, 120c are generally in an upper third of the mattress
101. In at least some embodiments, one or more of these upper body
force sensors 120a, 120b, 120c are located approximately sixteen to
nineteen inches from the top side 102 of the mattress 101. In some
embodiments, the upper body force sensors 120a, 120b, 120c may be
located in the range of twelve to twenty-four inches from the top
side 102 of the mattress 101.
[0047] In the example embodiment illustrated, the upper body force
sensors 120a, 120b, 120c include three force sensors: a first upper
body force sensor 120a, a second upper body force sensor 120b, and
a third upper body force sensor 120c. The first upper body force
sensor 120a is the left-most upper body force sensor in the set
150, 152 and the third upper body force sensor 120c is the
right-most upper body force sensor in the set 150, 152. The second
upper body force sensor 120b may be located along a line that is
midway between the first upper body force sensor 120a and the third
upper body force sensor 120c. More particularly, the second upper
body force sensor 120b may be equidistant from the first upper body
force sensor 120a and the third upper body force sensor 120c. In at
least some embodiments, the second upper body force sensor 120b
associated with the left portion 112 of the mattress may be midway
between the left side 106 and the center line 110. Similarly, the
second upper body force sensor 120b associated with the right
portion 114 of the mattress may be midway between the right side
108 and the center line 110.
[0048] The first upper body force sensor 120a and the third upper
body force sensor 120c may have a separation which is in the range
of eight to fifteen inches, in at least some embodiments. In one
example embodiment, the first upper body force sensor 120a and the
third upper body force sensor 120c may have a twelve inch
separation.
[0049] In at least some embodiments, the upper body force sensors
120a, 120b, 120c may be located at differing distances from the top
side 102 of the mattress 101. In the example illustrated, the first
upper body force sensor 120a and the third upper body force sensor
120c are both located at common distances from the top side 102 of
the mattress 101. The second upper body force sensor 120b is
located at a different distance from the top side 102 than the
first and third upper body force sensors 120a, 120c. More
specifically, in the example illustrated, the second upper body
force sensor 120b is relatively further from the top side 102 than
are the first and third upper body force sensors 120a, 120c. By
placing the upper body sensor which is in the middle of the other
two sensors at a different distance from the top side 102 than the
other upper body sensors, the area of coverage of the upper body
sensors may be increased. That is, this arrangement may provide a
larger coverage area for the upper body sensors than an embodiment
where all of the upper body sensors are equidistant from the top
side 102.
[0050] The example of FIG. 1 includes three upper body force
sensors in each sensor set 150, 152. The sensor sets 150, 152 may
include a greater or lesser number of upper body force sensors in
other embodiments.
[0051] In the example illustrated, each sensor set 150, 152 also
includes one or more middle body force sensors 120d, 120e, 120f.
These middle body force sensors 120d, 120e, 120f are located
generally nearer the middle of an occupant's body; for example,
near their lower back region. The middle body force sensors 120d,
120e, 120f are generally in a middle third of the mattress 101. In
at least some embodiments, one or more of these middle body force
sensors 120d, 120e, 120f is located approximately thirty one to
thirty three inches from the top side 102 of the mattress 101. In
some embodiments, the middle body force sensors 120d, 120e, 120f
may be located in the range of twenty nine to thirty six inches
from the top side 102 of the mattress 101.
[0052] In the example embodiment illustrated, the middle body force
sensors 120d, 120e, 120f include three force sensors: a first
middle body force sensor 120d, a second middle body force sensor
120e, and a third middle body force sensor 120f. The first middle
body force sensor 120d is the left-most middle body force sensor in
the set 150, 152 and the third middle body force sensor 120f is the
right-most middle body force sensor in the set 150, 152. The second
middle body force sensor 120e may be located along a line that is
midway between the first middle body force sensor 120d and the
third middle body force sensor 120f. More particularly, the second
middle body force sensor 120e may be equidistant from the first
middle body force sensor 120d and the third middle body force
sensor 120f. In at least some embodiments, the second middle body
force sensor 120e associated with the left portion of the mattress
may be midway between the left side 106 and the center line 110.
Similarly, the second middle body force sensor 120e associated with
the right portion 114 of the mattress may be midway between the
right side 108 and the center line 110.
[0053] The first middle body force sensor 120d and the third middle
body force sensor 120f may have a separation which is in the range
of eight to fifteen inches, in at least some embodiments. In one
example embodiment, the first middle body force sensor 120d and the
third middle body force sensor 120f may have a twelve inch
separation.
[0054] In at least some example embodiments, the middle body force
sensors 120d, 120e, 120f may be located at differing distances from
the top side 102 of the mattress 101. In the example illustrated,
the first middle body force sensor 120d and the third middle body
force sensor 120f are both located at common distances from the top
side 102 of the mattress 101. The second middle body force sensor
120e is located at a different distance from the top side 102 than
the first and third middle body force sensors 120d, 120f. More
specifically, in the example illustrated, the second middle body
force sensor 120e is relatively further from the top side 102 than
are the first and third middle body force sensors 120d, 120f. As
noted above in the discussion of the upper body force sensors, by
placing the middle body sensor which is in located between the
other two middle body force sensors at a different distance from
the top side 102 than the other middle body sensors, the area of
coverage of the middle body sensors may be increased. That is, this
arrangement may provide a larger coverage area for the middle body
sensors than an embodiment where all of the middle body sensors are
equidistant from the top side 102.
[0055] The example of FIG. 1 includes three middle body force
sensors in each sensor set 150, 152. The sensor sets 150, 152 may
include a greater or lesser number of middle body force sensors in
other embodiments.
[0056] In the example illustrated, each sensor set 150, 152 further
includes one or more lower body force sensors 120g, 120h. The lower
body force sensors 120g, 120h are generally located in a leg region
of the mattress. The leg region of the mattress is a region that is
associated with an occupant's legs. That is, the leg region is a
region where a person of average size would place their legs on the
mattress. The average size of a person may, for example, be
region-specific to account for differing height averages in
different parts of the world.
[0057] The lower body force sensors 120g, 120h are generally in a
lower third of the mattress 101. In at least some embodiments, one
or more of these lower body force sensors 120g, 120h is located
approximately fifty to fifty five inches from the top side 102 of
the mattress 101. In some embodiments, the lower body force sensors
120g, 120h may be located in the range of forty eight to fifty
eight inches from the top side 102 of the mattress 101.
[0058] In the example embodiment illustrated, the lower body force
sensors 120g, 120h include two force sensors: a first lower body
force sensor 120g and a second lower body force sensor 120h. The
first lower body force sensor 120g is the left-most lower body
force sensor in the set 150, 152 and the second lower body force
sensor 120h is the right-most lower body force sensor in the set
150, 152.
[0059] The first lower body force sensor 120g and the second lower
body force sensor 120h may have a separation which is in the range
of eight to fifteen inches, in at least some embodiments. In one
example embodiment, the first lower body force sensor 120g and the
second lower body force sensor 120h may have a twelve inch
separation.
[0060] The example of FIG. 1 includes two lower body force sensors
in each sensor set 150, 152. The sensor sets 150, 152 may include a
greater or lesser number of lower body force sensors in other
embodiments.
[0061] The force sensors 120a-120h may be of a variety of different
forms. In at least some embodiments, the force sensors 120a-120h
may include force sensitive resistors. A force sensitive resistor
is a material whose resistance changes when a force is applied. In
at least some embodiments, the force sensitive resistors may be
used in a voltage divider circuit. By way of example, in at least
some embodiments, the force sensitive resistor may be a model 402
force sensitive resistor from Interlink Electronics.TM.. Other
force sensors could be pressure sensitive foams (such as a
polyurthethane foam doped with carbon) or conductive
threads/fabrics that change resistance with deformation, as an
example.
[0062] Furthermore, in other embodiments, other sensors could be
used to sense movement and position of an occupant instead of or in
addition to the force sensors 120a-120h. For example, in some
embodiments, one or more accelerometers could be embedded into the
mattress.
[0063] The layout of the sensors described with reference to FIG. 1
may, in at least some embodiments, be varied from that described
and claimed above to account for variations in the sizes of
occupants. For example, the layout may be varied to account for
regional-based differences, age-based differences and/or
gender-based differences. For example, in one embodiment, a sensor
set 150, 152 may be arranged to accommodate a female of average
size. In one embodiment, a sensor set 150, 152 may be arranged to
accommodate a male of average size. In some embodiments, the
arrangement of sensors may be customized for an individual. For
example, measurements of an individual may be obtained and the
force sensors 120a-120h arranged in accordance with the obtained
measurements. That is, a processor associated with a manufacturing
system used to manufacture the sleep system may determine sensor
locations based on the measurements. The measurements may, for
example, be obtained by performing an image-based analysis on a
photograph of the individual. In other embodiments, the
measurements may be manually obtained an input into the
manufacturing system using an input device.
[0064] Referring briefly to FIG. 2, a cross-section of the mattress
101 taken along line 2-2 of FIG. 1 is illustrated. The cross
section of the mattress illustrates the embedding of sensors within
the mattress. As illustrated in FIG. 2, the mattress may be
composed of one or more internal supporting layers which generally
provide support to an occupant of the mattress. The internal
support layers may include foam layers and/or coils. Other
supporting materials may be used in other embodiments. In the
example illustrated, the mattress 101 is constructed of three foam
layers 212, 210, 208. A lower foam layer 212 is the thickest foam
layer in the example. This lower foam layer 212 supports a middle
foam layer 210. The middle foam layer 210 may support an upper foam
layer 208. The various foam layers may have different softness
ratings. That is, the firmness of the foam layers may differ and
some of the foam layers may have different indentation force
deflection (IFD) ratings than other of the foam layers. For
example, the upper foam layer 208 may be softer than the lower foam
layer 212 to provide a pillow-top effect.
[0065] The supporting layer(s) of the mattress may be enclosed by a
sock layer 207. The sock layer 207 is an internal casing and is
typically a fabric. The sock layer 207 surrounds the supporting
layers and is, itself, surrounded by a ticking layer 206 (which may
be referred to as the "ticking").
[0066] The ticking layer 206 is the outermost layer of the mattress
101. That is, the ticking is the final layer of the mattress which
encases the other layers of the mattress. The ticking is typically
constructed of a durable fabric.
[0067] As illustrated in FIG. 2, in at least some embodiments,
sensors, such as the force sensors 120a-120h described above
(and/or a body temperature sensor 122 and/or a humidity sensor 124
which will be described in greater detail below) may be embedded
within the mattress. That is, these sensors may be disposed
internally within the mattress 101. In some embodiments, these
sensors may be attached to an internal side of the ticking layer
206. In other embodiments, these sensors may be attached to the
sock layer 207 of the mattress 101. In some embodiments, these
sensors may be attached to the ticking layer 206 or the sock layer
207 using an adhesive, such as a glue. Accordingly, in at least
some embodiments, the sensing components of the sleep system 100 is
non-contact; that is, the user does not directly contact the
sensors.
[0068] To facilitate an understanding of mattress flipping and
rotation, which will be discussed below with reference to FIG. 10,
two additional sides of the mattress will be described--an upper
side 260 and a lower side 262. The upper side 260 is the side that
supports an occupant and the lower side 262 supports the mattress
itself. The lower side may rest on a floor, frame or box
spring.
Temperature Sensor(s)
[0069] Referring again to FIG. 1, the sensor sets 150, 152 may
include other sensors instead of or in addition to the force
sensors described above. For example, in at least some embodiments,
a body temperature sensor 122 may be included in one or more of the
sensor sets 150, 152. The body temperature sensor 122, which is
embedded into the mattress, is positioned to obtain temperature
readings associated with an occupant of the mattress. That is, the
body temperature sensor 122 detects an occupant's body
temperature.
[0070] In order to accurately measure an occupant's body
temperature, the body temperature sensor 122 is placed in a region
of the mattress in which an occupant frequently sleeps. In at least
some embodiments, the body temperature sensor 122 may be located in
a middle body region of the mattress 101. The middle body region of
the mattress 101 is a region of the mattress that is located
generally nearer the middle of an occupant's body; for example,
near their lower back region. The body temperature sensor 122 may
generally be in a middle third of the mattress 101. In at least
some embodiments, the body temperature sensor 122 is located
approximately thirty one to thirty three inches from the top side
102 of the mattress 101. In some embodiments, the body temperature
sensor 122 may be located in the range of twenty nine to thirty six
inches from the top side 102 of the mattress 101.
[0071] The body temperature sensor 122 may, in at least some
embodiments, be located at or near the middle of the left portion
112 and/or the right portion 114 of the mattress 101. In at least
some embodiments, the body temperature sensor 122 associated with
the left portion of the mattress may be approximately midway (i.e.
within a two inch variation) between the left side 106 and the
center line 110. Similarly, the body temperature sensor 122
associated with the right portion 114 of the mattress may be
approximately midway (i.e. within a two inch variation) between the
right side 108 and the center line 110. In some embodiments, such
as embodiments where the mattress is sized for a single occupant, a
body temperature sensor may be located near the center line 110
(i.e. within 2 inches of the center line 110).
[0072] The body temperature sensor 122 may be of a variety of
different types. In one embodiment, the body temperature sensor
includes a thermistor. A thermistor is a resistor whose resistance
is highly temperature-dependent. That is, the resistance of the
thermistor changes greatly due to changes in temperature. By way of
example, in at least one embodiment, the temperature sensor 122 may
be a model MCP9700 or TC1047 model thermistor from Microchip.TM..
It will be appreciated that other temperature sensors may also be
used.
[0073] In at least some embodiments, the body temperature sensor
122 may be located to be near at least one force sensor 120a-120h.
For example, the body temperature sensor 122 may be placed in an
area of the mattress which is defined by the middle body force
sensors 120d, 120e, 120f. In at least some embodiments, the body
temperature sensor 122 may be within five inches of at least one
force sensor. In the example illustrated, the body temperature
sensor 122 is located in proximity to the second middle body force
sensor 120e. That is, the body temperature sensor 122 and the
second middle body force sensor 120e are within five inches of one
another.
[0074] In at least some embodiments, before a processor (which will
be described in greater detail below) utilizes a temperature
reading obtained from the temperature sensor 122 for an operation
that relies upon an occupant's body temperature, it will determine
whether the body temperature sensor 122 has, in fact, been engaged
by an occupant's body when determining whether a temperature
reading represents a body temperature, the processor may analyze
the temperature reading. If the temperature is too low (i.e. if it
is less than a predetermined threshold), then the processor may
determine that the temperature sensor is not engaged and that the
temperature being reported by the temperature sensor is a room
temperature and not a body temperature. In at least some
embodiments in which a force sensor 120a-120h is located near the
body temperature sensor 122, data from the force sensor may be used
to determine whether the body temperature sensor 122 is likely
engaged by an occupant's body. For example, if the force being
reported by the force sensor 120a-120h nearest the body temperature
sensor 122 exceeds a predetermined threshold, then the processor
may determine that the body temperature sensor 122 is likely
engaged and is likely reporting a body temperature. If, however,
the force is less than a threshold, then the processor may
determine that the body temperature sensor 122 is not reporting a
body temperature.
[0075] In will be appreciated that, in at least some embodiments, a
plurality of temperature sensors 122 may be embedded into the
mattress at a plurality of different locations. For example, a
first temperature sensor may be located at a first location and a
second temperature sensor may be located at a second location.
[0076] Furthermore, as will be discussed in greater detail below
with reference to FIG. 3, in at least some embodiments, the sleep
system 100 may include a room temperature sensor which is located
to obtain temperature readings associated with the room where the
sleep system 100 is located so that the temperature of a sleep
environment may be assessed.
Humidity Sensor(s)
[0077] In at least some embodiments, the sensor sets 150, 152 may
also include one or more humidity sensors 124 which are embedded
into the mattress 101. In some embodiments, at least one of the
humidity sensors 124 may be a body humidity sensor 124. The body
humidity sensor 124 may be used to obtain humidity readings which
indicate an amount of perspiration of the occupant. Accordingly,
the body humidity sensor 124 may, in at least some embodiments, be
referred to as a perspiration sensor or a sweat sensor.
[0078] To detect humidity caused by an occupant, the body humidity
sensor 124 may be placed at a location where it is aligned with an
occupant's typical or expected sleeping position. For example, the
humidity sensor 124 may be placed in a region of the mattress in
which an occupant frequently sleeps. In at least some embodiments,
the humidity sensor 124 may be located in the middle body region of
the mattress 101. The humidity sensor 124 may generally be in a
middle third of the mattress 101. In at least some embodiments, the
humidity sensor 124 is located approximately thirty one to thirty
three inches from the top side 102 of the mattress 101. In some
embodiments, the humidity sensor 124 may be located in the range of
twenty nine to thirty six inches from the top side 102 of the
mattress 101.
[0079] The humidity sensor 124 may, in at least some embodiments,
be located at or near the middle of the left portion 112 and/or the
right portion 114 of the mattress 101. In at least some
embodiments, the humidity sensor 124 associated with the left
portion of the mattress may be approximately midway (i.e. within a
two inch variation) between the left side 106 and the center line
110. Similarly, a humidity sensor 124 associated with the right
portion 114 of the mattress may be approximately midway (i.e.
within a two inch variation) between the right side 108 and the
center line 110. In some embodiments, such as embodiments where the
mattress is sized for a single occupant, a humidity sensor 124 may
be located near the center line 110 (i.e. within 2 inches of the
center line 110).
[0080] The humidity sensor 124 may be of a variety of different
types. By way of example, in at least one embodiment, the humidity
sensor 124 may be a Honeywell.TM. model HIH-5030 or model HCH-1000
humidity sensor.
[0081] In at least some embodiments, the humidity sensor 124 may be
located to be near at least one force sensor 120a-120h. For
example, in at least some embodiments, the humidity sensor 124 may
be placed in an area of the mattress which is defined by the middle
body force sensors 120d, 120e, 120f. In at least some embodiments,
the humidity sensor 124 may be within five inches of at least one
force sensor. In the example illustrated, the humidity sensor 124
is located in proximity to the second middle body force sensor
120e. That is, the humidity sensor 124 and the second middle module
force sensor 120e are within five inches of one another.
[0082] In at least some embodiments, before a processor interprets
a reading from the humidity sensor as a perspiration reading
(and/or incontinence reading) for an occupant of the mattress, it
will determine whether the humidity sensor 124 has, in fact, been
engaged by an occupant's body. In at least some embodiments in
which a force sensor 120a-120h is located near the humidity sensor
124, data from the force sensor may be used to determine whether
the humidity sensor 124 is likely engaged by an occupant's body.
For example, if the force being reported by the force sensor
120a-120h nearest the humidity sensor 124 exceeds a predetermined
threshold, then the processor may determine that the humidity
sensor 124 is likely engaged and is likely reporting a perspiration
reading (i.e. a reading representing humidity caused by a user
perspiring). If, however, the force is less than a threshold, then
the processor may determine that the humidity sensor 124 is not
reporting a perspiration reading (i.e. that the humidity being
reported is not caused by a user perspiring) or incontinence
reading.
[0083] As will be described below with reference to FIG. 10, the
humidity sensor 124 may also, in at least some embodiments, be used
to assess the health of the mattress itself. More particularly, a
processor may monitor the humidity level associated with the
mattress and may generate an alert if the humidity level exceeds a
threshold and/or if the humidity level exceeds a threshold for at
least a predetermined period of time. In embodiments in which the
humidity sensor 124 is used to assess the mattress health, the
humidity sensor 124 may have a different location than that noted
above. More particularly, in such embodiments the humidity sensor
124 may not be located in a location that is typically associated
with an occupant. However, in other embodiments, the humidity
sensor 124 used for assessing mattress health may be located in a
location associated with an occupant.
[0084] In some embodiments, a humidity sensor 124 may be located in
a region associated with occupant's middle body and, more
particularly, to a region which would typically be near the
occupant's urethra. In at least some such embodiments, the humidity
sensor 124 could be used to detect a bedwetting condition (which
may also be referred to as an incontinence condition). That is, if
the humidity level reported by the humidity sensor exceeds a
predetermined threshold, then an associated processor may determine
that an occupant has urinated in bed.
[0085] In will be appreciated that, in at least some embodiments, a
plurality of humidity sensors 124 may be embedded into the mattress
at a plurality of different locations. For example, a first
humidity sensor may be located at a location associated with a
occupant's genitals and may be used to detect bedwetting and a
second humidity sensor may be located at a location in which it
would be likely to be engaged by an occupant's back so that it
could be used to detect excessive perspiration from the occupant's
back. Similarly, in some embodiments, another humidity sensor could
be located at another location where it is unlikely to be engaged
by the occupant. This humidity sensor could be used for detecting a
humidity level associated with mattress health.
[0086] Furthermore, as will be discussed in greater detail below
with reference to FIG. 3, in at least some embodiments, the sleep
system 100 may include a room humidity sensor 330 which is located
to obtain humidity readings associated with the room where the
sleep system 100 is located so that the humidity of a sleep
environment may be assessed.
[0087] In at least some embodiments, the body temperature sensor
122 and the humidity sensor 124 embedded into the mattress 101 are
provided on a common printed circuit board 128 and/or a flexible
circuit board, which may provide further comfort for the occupant.
The printed circuit board 128 may, for example, facilitate
connection of the sensors to one or more transport mediums 140
(e.g. wires) which may connect the sensors to one or more
processors.
Transport Mediums
[0088] As illustrated in FIG. 1, the various sensors (such as force
sensors 120a-120h, temperature sensors 122 and/or humidity sensors
124) that are embedded into the mattress 101 may be connected to
one or more processors 130a, 130b, 117 using one or more transport
mediums 140, which are embedded into the mattress 101. That is, the
transport mediums 140 are internally run within the mattress so
that an occupant cannot access the transport mediums 140.
[0089] In the example illustrated in FIG. 1, only a single
transport medium 140 on each side of the mattress has been labelled
to avoid clutter. However, it will be appreciated that transport
mediums may connect each sensor to at least one processor and, in
at least some embodiments, a power source 312 (FIG. 3).
[0090] The transport mediums 140 are conductive mediums that may be
used to transmit an electrical signal from the sensors to the
processor(s) 130a, 130b, 117.
[0091] The transport mediums 140 may, in at least some embodiments,
include wires. In some embodiments, at least some of the wires
which run through a region of the mattress where an occupant might
be expected to contact during sleep are small gauge wires (for
example, up to 20 American Wire Gauge (AGW)) to ensure that the
occupant cannot feel the wires.
[0092] In one embodiment, the transport mediums 140 may include
conductive thread, fabric, or ink/paint. Conductive thread or
fabric is thread or fabric that is composed of a material which
conducts an electrical signal. The conductive thread provides an
electrical connection between one or more sensors (such as a force
sensor 120a-120h) to the processor(s) 130a, 130b, 117. The
conductive thread may be sewn into a layer of the mattress 101,
such as a sock layer 207 or a ticking layer 206 of the mattress 101
(which are described above with reference to FIG. 2). In at least
some embodiments, a conductive thread may be used which is a
silver-plated nylon yarn.
[0093] In FIG. 1, it appears that a single transport medium
connects to each sensor. In practice, a plurality of transport
mediums 140 may connect to each sensor. For example, one or more
transport mediums may connect a sensor to a power source 312 (FIG.
3) and another one or more transport mediums may be used for
transmitting data.
Processors
[0094] The sleep system 100 includes one or more processors 130a,
130b, 117. The processors 130a, 130b, 117 may be used to analyze
data obtained from sensors associated with the sleep system 100,
such as the force sensors 120a-120h, the temperature sensor(s) 122,
the humidity sensor(s) 124, a microphone 334 (FIG. 3), a light
sensor 336 (FIG. 3), a dust sensor 338 (FIG. 3), a room humidity
sensor 330 (FIG. 3) and/or a room temperature sensor 332 (FIG.
3).
[0095] In the embodiment illustrated, the sleep system includes a
plurality of processors 130a, 130b, 117. More specifically, each
sensor set 150, 152 is associated with a separate processor, which
are microcontrollers 130a, 130b, in the example. In the example
embodiment illustrated, the microcontrollers 130a, 130b are both
electrically connected to a main processor 117.
[0096] The microcontrollers 130a, 130b may include small processors
which are capable of doing simple calculations and data
manipulation. Tasks that are more processing-intensive may be
performed by the main processor 117 and/or by another processor
which may be provided on a remote server or a mobile device.
[0097] Each microcontroller 130a, 130b may be connected to a
plurality of sensors via one or more transport mediums 140. These
transport mediums 140 may be of the type described above. For
example, in at least some embodiments, the microcontrollers 130a,
130b may connect to the sensors using conductive thread.
[0098] In the example illustrated, each microcontroller 130a, 130b
is connected to all of the sensors in one of the sensor sets 150,
152. That is, a first microcontroller 130a is connected to the
sensors in the first set 150, which is the set that generally
provides coverage on the left portion 112 of the mattress 101 and a
second microcontroller 130b is connected to the sensors in the
second set 152, which is the set that generally provides coverage
on the right portion 114 of the mattress 101. More particularly,
the first microcontroller 130a is connected to force sensors
120a-120h on the left portion 112 of the mattress 101 and, in at
least some embodiments, a body temperature sensor 122 and/or a
humidity sensor 124 associated with the left portion 112 of the
mattress 101. Similarly, the second microcontroller 130b is
connected to force sensors 120a-120h on the right portion 114 of
the mattress 101 and, in at least some embodiments, a body
temperature sensor 122 and/or a humidity sensor 124 associated with
the right portion 114 of the mattress 101. Since each
microcontroller 130a, 130b services a set of sensors associated
with a particular side of the mattress in the example, the
combination of a microcontroller 130a and the sensors which that
microcontroller 130a services may be referred to as a sensing array
or a sensor block. Accordingly, the first microcontroller 130a and
the first sensor set 150 may be referred to as a first sensing
array or a left occupant sensing array 302 (FIG. 3), in at least
some embodiments. Similarly, the second microcontroller 130b and
the second sensor set 152 may be referred to as a second sensing
array or a right occupant sensing array 304 (FIG. 3), in at least
some embodiments.
[0099] The microcontrollers 130a, 130b may connect to the main
processor 117 using one or more transport mediums. In some
embodiments, these transport mediums may be conductive thread.
However, in other embodiments, these transport mediums may be
wires. Thus, the main processor is coupled with the sensors via the
microprocessors 130a, 130b, in at least some embodiments.
[0100] In at least some embodiments, the microcontrollers 130a,
130b may communicate with the main processor 117 over more or more
buses, which are provided over the transport mediums connecting the
microcontrollers 130a, 130b to the main processor 117. In some
embodiments, the microcontrollers 130a, 130b may communicate with
the main processor 117 over and Inter-Integrated Circuit (I.sup.2C)
bus. The I.sup.2C bus may use two bidirectional open-drain lines
for communications, including a serial data line (SDA) and a serial
clock (SCL). These lines may be pulled up with resistors, which may
be 4.7 kilo-ohm resistors, in some embodiments.
[0101] Depending on the type of sensors and processors used, the
processors may interface with one or more analog to digital
converters (ADC) and/or one or more digital to analog converters
(DAC), which may connect to one or more of the processors 130a,
130b, 117. The ADC may, for example, be used to convert an analog
signal generated by a sensor (such as a force sensor 120a-120h)
into a digital signal which may be input to a processor (such as
the microcontrollers 130a, 130b and/or the main processor 117).
[0102] The main processor 117 may act as a master controller and
the microcontrollers 130a, 130b may act as slaves. In at least some
embodiments, the slave microcontrollers 130a, 130b are configured
to include identifying information in communications which are sent
by the microcontrollers 130a, 130b over the bus to the main
processor 117. For example, a first byte of data sent to the main
processor 117 from the microcontrollers 130a, 130b may be used to
identify the microcontroller 130a, 130b which sent that data. Such
identification allows for easy expansion of the system to
incorporate more sensors if needed.
[0103] The microprocessors 130a, 130b may be configured to
periodically collect data from the sensors in an associated sensor
set 150, 152. The microprocessor(s) 130a, 130b may collect data
from different types of sensors at different rates. For example, to
perform some of the analysis discussed below, a large resolution in
the time domain may be required for force sensor data. Accordingly,
in some embodiments, data from force sensors 120a-120h may be
collected at a period that is in the range of 80 to 120 ms. In some
embodiments, data from the force sensors 120a-120h may be collected
every 100 ms. However, other sensors, such as the body temperature
sensor 122 and/or the humidity sensor 124 may not require as high a
resolution in the time domain. Thus, the microprocessor(s) 130a,
130b may sample the body temperature sensor 122 and/or the humidity
sensor 124 at a lower rate than the force sensors 120a-120h. For
example, in some embodiments, data from the body temperature sensor
122 and/or the humidity sensor 124 may be collected at a period
that is in the range of 2500 to 4500 ms.
[0104] While FIG. 1 illustrates an embodiment in which three
processors are utilized (including a main processor 117 and two
microcontrollers 130a, 130b), in other embodiments a greater or
lesser number of processors may be used. For example, in some
embodiments, the main processor 117 may perform some data
calculations and manipulations and may output the data to a
connected peripheral which contains a further processor which
performs additional analysis on the data.
[0105] The microcontroller(s) 130a, 130b are embedded into the
mattress 101 in the illustrated embodiment. For example, the
microcontroller(s) 130a, 130b may be attached to a sock layer 207
of the mattress 101 or an interior side of the ticking layer 206 of
the mattress. These layers are described in greater detail above
with reference to FIG. 2. The microcontroller(s) are disposed
internally within the mattress so that they cannot be viewed by the
occupant of the mattress 101. The microcontrollers may, in at least
some embodiments, be provided on PCBs or flexible PCBs.
[0106] Furthermore, in some embodiments, some of the analysis
described herein may be performed using a processor that is remote
from the mattress 101. For example, as will be described below with
reference to FIG. 3, the sleep system 100 may be equipped with a
communication subsystem, such as a wireless communication subsystem
370. The wireless communication subsystem may, for example, be a
WiFi connection and/or a Bluetooth.TM. connection. This connection
may be used for sending data to a remote server or computer, which
contains a processor. By way of example, in some embodiments, data
may be collected and periodically sent to the remote server or
computer for analysis. For example, the wireless communication
subsystem may provide a connection between the main processor 117
and a mobile device 1200 (FIG. 12) such as a smartphone or tablet
computer (or a computer of another type). The mobile device 1200
may include a processor 1217 (FIG. 12) which may be associated with
computer executable instructions which configure the processor to
perform at least some of the analysis described below. Further, in
some embodiments, data may be sent over the Internet to a server.
This data may be sent directly from the main processor 117 to the
remote server (i.e. via the wireless communication subsystem 370),
or may be sent by engaging a mobile device 1200 or other computer
which may have Internet connectivity and which may forward the data
to the remote server. Thus, the remote server may include a
processor which may be associated with computer executable
instructions which configure the processor to perform at least some
of the analysis described below.
[0107] The main processor 117 is, in some embodiments, provided
internally within the mattress 101. In the example illustrated, the
main processor 117 is provided in a central processing unit 132
which is integrated with the mattress 101. The central processing
unit 132 may be provided at one end of the mattress 101. In the
example illustrated, the central processing unit 132 is provided at
a bottom side 104 of the mattress 101. However, the central
processing unit 132 may be provided at different locations in other
embodiments.
[0108] By way of further example, in some embodiments the central
processing unit 132, or a portion thereof, may be provided at a
location that is external to the mattress 101. For example, the
central processing unit 132 (or a portion thereof) may be provided
as a peripheral which connects to other components of the sleep
system 100 (such as the microcontrollers 130a, 130b) either via a
wired or wireless connection. The peripheral may, for example, be
configured to rest on a table, such as a night table, located near
the mattress 101.
Machine-Readable Code(s)
[0109] As will be discussed in greater detail below, in some
embodiments, the sleep system 100 may be configured to communicate
with an associated mobile device 1200 (FIG. 12). The mobile device
1200 may, for example, be a smartphone or tablet computer.
[0110] In at least some embodiments, to facilitate download of a
mattress monitoring application 1290 (FIG. 12) onto the mobile
device and/or setup of the mattress monitoring application 1290 on
the mobile device, one or more machine readable codes 180a, 180b
may be affixed to the mattress 101. This code may, for example, be
a quick response (QR) code. The machine-readable code may, in at
least some embodiments be unique to the mattress. That is, the
machine-readable code may uniquely identify the mattress 101 from
other mattresses. In at least some embodiments, the
machine-readable code 180a, 180b is readable by the mobile device
1200 to associate the mobile device with the mattress. In some
embodiments, this may allow the mobile device to communicate with a
server and to register the mattress in a user profile maintained by
the server.
[0111] In some embodiments, both the left and right portions 112,
114 of the mattress 101 may include separate machine-readable codes
180a, 180b which may be used to associate a mobile device 1200 with
a specific side of the mattress. Each of these machine-readable
codes is associated with a separate portion 112, 114 of the
mattress 101. For example, a first code 180a may be located at a
left portion 112 of the mattress and associated with the left
portion 112 and a second code 180b may be located at a right
portion 114 of the mattress and associated with the right portion
114. A user of a mobile device 1200 (FIG. 12) may use a camera 1280
(FIG. 12) on that device to scan the code 180a, 180b. The codes
180a, 180b uniquely identify the mattress from other mattresses,
and each of the codes uniquely identifies the side of the mattress
associated with that code. For example, the first code 180a may
identify the left side and the second code 180b may identify the
right side.
[0112] In such embodiments, the code 180a, 180b may be used by the
mobile device 1200 to associate the mobile device 1200 with a
specific side of the mattress. That is, an occupant who sleeps on
the left portion 112 may scan the code 180a associated with the
left portion. In at least some embodiments, by doing so the mobile
device 1200 will then obtain and/or display information obtained
from the sleep system about the left portion of the mattress. For
example, sleep state information and/or raw data generated from a
first sensor set 150 located at the left portion may be retrieved
by the mobile device which has scanned the code 180a on the left
portion, but sleep state information and/or raw data generated from
the second sensor set 152 located at the right portion may not be
retrieved by the mobile device which has scanned the code 180a on
the left portion. Accordingly, in at least some embodiments, a
mobile device 1200 may only retrieve and/or display information
associated with a portion of the bed for which it has scanned the
associated code 180a, 180b.
[0113] In at least some embodiments, the codes may have encoded
therein a location where the mobile device 1200 (FIG. 12) may
download the mattress monitoring application 1290. This location
may, for example, be a server location such as the location of a
file on an application store, such as Google Play' or Apple' app
store.
[0114] In some embodiments, the sleep system 100 may be equipped
with one or more wireless tags which store the machine readable
code(s) referred to above. For example, in some embodiments, a near
field communication (NFC) tag or radio frequency identification
(RFID) tag may be provided on the sleep system 100. The tag may be
read by a mobile device 1200 (FIG. 12) to cause the mobile device
to perform one or more of the functions described above (e.g. to
cause the mobile device to download the mattress monitoring
application 1290 (FIG. 12) onto the mobile device and/or setup the
mattress monitoring application 1290, and/or to associate the
mobile device with a specific side of the mattress). In at least
some embodiments, the sleep system 100 may include a plurality of
tags and each tag may be physically located near a separate side of
the mattress. In such embodiments, when the mobile device 1200
scans the tag, it may associate the mobile device 1200 with the
side where the tag was located.
Block Diagram of Sleep System
[0115] Referring now to FIG. 3, a block diagram of the sleep system
100 is illustrated. The block diagram includes components discussed
above.
[0116] As illustrated, the sleep system 100 includes a plurality of
occupant monitoring sensors. The occupant monitoring sensors
include force sensors 120a-120h, a body temperature sensor 122 and
a humidity sensor 124. In the example illustrated, the sensors are
divided into two sensor sets 150, 152. A first sensor set 150 is
associated with a left portion 112 of the mattress 101 and is
included in a left occupant sensing array 302. A right sensor set
152 is associated with a right portion 114 of the mattress and is
included in a right occupant sensing array 304. Each of the sensing
arrays 302, 304 in the example includes an associated
microcontroller 130a, 130b, which receives sensor data from the
sensors in the sensing array 302, 304 associated with the
microcontroller 130a, 130b.
[0117] In the example, each sensor set 150, 152 includes a
plurality of force sensors 120a-120h, a body temperature sensor 122
and a humidity sensor 124. The sensor sets 150, 152 may include
other types of sensors instead of or in addition to the sensors
described above. Further, in some embodiments, one or more of the
sensors illustrated in FIG. 3 may be omitted. The sensors may, for
example, generate an electric signal which includes sensor data and
may provide the electric signal to a processor, such as the
microcontroller 130a, 130b and/or the main processor 117.
[0118] The sensors in the sensor sets 150, 152 may be arranged in
the manner described above with reference to FIGS. 1 and 2.
[0119] As noted above, transport mediums 140 may connect the
sensors in a sensing array 302, 304 to a processor, such as an
associated microcontroller 130a, 130b.
[0120] The microcontrollers 130a, 130b include a processor and
associated memory. The microcontrollers 130a, 130b are coupled with
a main processor 117. The main processor is coupled with a memory
372. The memory associated with the microcontrollers 130a, 130b and
the memory associated with the main processor 117 may store
processor-executable instructions which configure the associated
processor to perform a method, such as one or more of the methods
described below.
[0121] The memory 372 associated with the main processor 117 is, in
the embodiment illustrated, external to the main processor 117. In
other embodiments, the memory, the memory may be internal memory of
the main processor 117.
[0122] The memory 372 associated with the main processor 117 and
the memories associated with the microcontrollers 130a, 130b may
take a variety of forms and may include a plurality of different
types of memories. For example, in some embodiments, flash memory
may be utilized. In some embodiments, random access memory (RAM)
may be used. It will be appreciated that any one of the processors
may be coupled with memory of a plurality of types. For example,
the main processor 117 may use both flash memory and RAM.
[0123] A memory 372 coupled with a processor (such as the main
processor 117) may be used, in at least some embodiments, for
storing data obtained or derived from the sensors. For example,
information derived from the sensor data may be stored in the
memory 372 for further analysis or reporting. For example, various
scores that may be determined in accordance with some embodiments
described below may be stored in the memory 372. These scores may,
for example, include a sleep score, a sleep environment score, a
mattress health score, etc. Further, in at least some embodiments,
a processor may use the memory 372 to store sleep state information
for an occupant of the mattress 101. The sleep state information
may be of various types. For example, in at least some embodiments,
the processor may store information regarding times associated with
various sleep stages of the occupant. For example, the time when a
user fell asleep and/or woke up may be recorded in the memory.
Similarly, in at least some embodiments, sleep disorder information
for an occupant may be stored in the memory 372. This information
may indicate whether a user has or is likely to have a sleep
disorder. The sleep disorder may, for example, include any one or
combination of: insomnia, narcolepsy, sleep apnea, bruxism, delayed
sleep phase syndrome, advanced sleep phase syndrome, periodic limb
movement disorder, sleep walking, sleep talking, bed wetting, etc.
Techniques which may be used to allow one or more of the processors
117 to detect such conditions are described in greater detail below
with reference to FIG. 9.
[0124] Furthermore, in some embodiments, the processor(s) 117 may
store in memory 372 information about the health of the mattress.
This information may, for example, include, for example, mattress
health information. Mattress health information is information
about the health of the mattress 101. The mattress health
information may, for example, quantify the usage of the mattress
over its lifetime (i.e. since manufacture of the mattress),
quantify the usage of the mattress since a maintenance event (such
as the usage since a last flip or rotation of the mattress, the
usage since the last vacuuming of the mattress, the usage since the
last change of bedding, the usage since the last deodorizing and/or
disinfecting of the mattress), and/or may be based on the humidity
level associated with the mattress. Techniques which may be used to
allow one or more of the processors to detect such conditions are
described in greater detail below with reference to FIG. 10.
[0125] Furthermore, in some embodiments, the processor(s) may store
in memory information about a sleeping environment associated with
the mattress 101. The sleeping environment information may, for
example, include a measure of a humidity level in the room where
the mattress is located, or a measure of a temperature level in the
room where the mattress is located. Other sleeping environment
information may be stored in the memory in other embodiments.
[0126] In some embodiments, raw sensor data may be stored in the
memory 372 for further analysis or reporting. This raw sensor data
may, for example, include force sensor data (i.e. data obtained
from one or more of the force sensors 120a-120h), body temperature
sensor data (i.e. data obtained from a body temperature sensors
122), humidity sensor data (i.e. data obtained from a humidity
sensors 124), dust sensor data (i.e. data obtained from a dust
sensor), audio data (which may be data obtained from a microphone
334), light sensor data (i.e. data obtained from a light sensor),
room temperature sensor data (i.e. data obtained from a room
temperature sensor 332), and/or room humidity sensor data (i.e.
data obtained from a room humidity sensor 330), etc.
[0127] The sleep system 100 may include one or more output
interfaces 380. The output interface 380 may be used for outputting
information from the sleep system 100. In some embodiments, the
output interfaces 380 may include a display 390. The display 390
may, for example, be a liquid crystal display (LCD) or a display of
another type. In some embodiments, the display 390 may be a
touchscreen display. The touchscreen display may be used both as an
output interface and an input interface for receiving input at the
sleep system 100. The display 390 may be controlled by the main
processor 117 and used for providing a visual output of information
derived from one or more of the sensors. For example, in at least
some embodiments, the display 390 may, in one operating mode,
display a display screen which provides a score such as a sleep
score, a sleep environment (a.k.a. hygiene) score, a mattress
health score, etc. The output interfaces may be used for providing
feedback based on sleep state information determined at the sleep
system 100. Such sleep state information will be described in
greater detail below.
[0128] Accordingly, in at least some embodiments, a processor may
cause the display 390 to display sleep state information for an
occupant of the mattress 101. Such information may, for example,
indicate times when an occupant fell asleep, woke up, entered a
particular stage of sleep, etc.
[0129] In some embodiments, a processor 117 may cause a display 390
to display sleep disorder information for an occupant. As noted
above, this information may indicate whether a user has or is
likely to have a sleep disorder. The sleep disorder may, for
example, include and one or combination of: insomnia, narcolepsy,
sleep apnea, bruxism, delayed sleep phase syndrome, advanced sleep
phase syndrome, periodic limb movement disorder, sleep walking,
sleep talking, bed wetting, etc.
[0130] Furthermore, in some embodiments, the processor(s) may cause
the display 390 to display information about the health of the
mattress 101. As noted above, this information may, for example,
include an indication of when the mattress was last rotated and/or
flipped, an indication of a total amount of usage of a mattress
over its life, an indication of a total amount of usage of a
mattress since its last flip or rotation, and/or an indication of a
humidity level associated with the mattress. In at least some
embodiments, the processor may cause an alert to be displayed if it
determines that the humidity levels associated with the mattress
are likely to cause mattress health issues, such as mold.
Similarly, in some embodiments, the processor may cause a mattress
life indicator to be displayed. The mattress life indicator may
alert the occupant when it is time to replace the mattress. In at
least some embodiments, the processor may cause a mattress flip or
rotation indicator to be displayed. The mattress flip or rotation
indicator may alert the occupant when it is time to flip or rotate
the mattress.
[0131] Furthermore, in some embodiments, the processor(s) may cause
information about a sleeping environment associated with the
mattress 101 to be displayed. The sleeping environment information
may, for example, include a measure of a humidity level in the room
where the mattress is located, or a measure of a temperature level
in the room where the mattress is located. Other sleeping
environment information may be displayed in other embodiments.
[0132] The output interfaces 380 may also include one or more
wireless communication subsystems 370. The wireless communication
subsystem 370 may be coupled with the main processor 117 and used
to send data to or receive data from another system or device.
[0133] In at least some embodiments, the wireless communication
subsystems 370 may include a Bluetooth.TM. subsystem. The Bluetooth
subsystem is a short-range communication subsystem which may, for
example, use Bluetooth-formatted communications to connect with a
nearby paired device, such as a mobile device 1200 (FIG. 12)
including a smartphone or tablet computer. The mobile device may,
in at least some embodiments, have installed thereon a mattress
monitoring application which is configured to interface with the
sleep system 100. For example, the sleep system application may be
configured to use the data received from the sleep system 100 to
generate a display on a display 1290 (FIG. 12) of a mobile device
and/or a laptop or desktop computer. The display may display
information of the type described above as being displayed on the
sleep system's display 390. For example, various information about
an occupant's sleep, the sleeping environment and/or the mattress
health may be displayed.
[0134] In some embodiments, the wireless communication subsystems
370 may include a Wi-Fi subsystem and/or a cellular subsystem such
as a 3G, 4G or Long Term Evolution (LTE) network subsystem. The
Wi-Fi subsystem may be configured to communicate using a Wi-Fi
protocol. The Wi-Fi subsystem may, for example, provide
connectivity to the Internet via a router.
[0135] In at least some embodiments, the wireless communication
subsystem 370 allows the sleep system 100 to send data to another
device, server or system for further processing. For example, the
other device, server or system may be configured to perform one or
more of the methods described below, or a portion thereof.
[0136] The electrical components of the sleep system 100 (such as
the processor(s), sensors, etc.) may be connected to and receive
power from one or more power source 312. In some embodiments, the
sleep system 100 may include or be connectable to a power cable
which connects the sleep system 100 to a mains power source, which
may be an alternating current (AC) power source. In at least some
embodiments, an AC to DC (direct current) converter may be used to
convert the alternating current provided by the mains power source
to DC, which may be required by at least some of the electrical
components of the sleep system in some embodiments.
[0137] Further, in at least some embodiments, the power source 312
may include a battery, which may be inserted into a battery
interface. The battery may be included instead of or in addition to
a connection to a mains power source.
[0138] In at least some embodiments, the wireless communication
subsystem 370, the memory 372, the display 390 (and/or another
output interface 380), and/or the main processor 117, may be
provided in a central processing unit 132. The central processing
unit 132 may include a housing which houses the components of the
central processing unit 132. In some embodiments, the central
processing unit 132 may be included in the mattress 101. For
example, in the example embodiment of FIG. 1, the central
processing unit 132 is embedded into the mattress. The central
processing unit 132 may be located at a side of the mattress. Such
a location may provide less obstruction for signals sent and
received via the wireless communication subsystem 370 than
embodiments where the central processing unit 132 is more centrally
located. Further, such a location may allow the display 390 to
protrude from a side of the mattress 101 where it may be easily
viewed.
[0139] In the embodiment illustrated in FIG. 1, the central
processing unit 132 is located at a bottom side 104 of the mattress
101. In other embodiments, the central processing unit 132 may be
located at either the left side 106 or the right side 108 of the
mattress 101. In at least some embodiments, the central processing
unit is located away from a location of the mattress where the
occupant typically sleeps. Such locations may minimize the
interference on a wireless signal caused by the occupant.
[0140] In other embodiments, the central processing unit 132 or a
portion thereof, may be provided in an external peripheral which
may connect to the sleep system 100 through either a wired or
wireless connection. For example, in some embodiments, a cable may
connect the external peripheral to the sleep system 100. The
peripheral may, for example, be configured to rest on a flat
surface, such as a tabletop. By way of example, the external
peripheral may be placed on a nightstand in some embodiments.
[0141] Furthermore, in other embodiments, the components of the
central processing unit 132 may be physically separated, with some
of the components being provided in the mattress 101 and some of
the components being provided in a connected external peripheral.
In some such embodiments, both the mattress 101 and the external
peripheral may include a processor. One or both of these processors
may be configured to perform any one or more of the methods
described below.
[0142] The sleep system 100 may also include sensors associated
with a sleeping environment sensing array 306. The sleeping
environment sensing array is configured to obtain information about
the environment where the mattress 101 is located. In at least some
embodiments, the sleeping environment sensing array 306 may be
provided in the central processing unit 132. As noted above, the
sleeping environment sensing array 306 may be provided in the
mattress 101 itself or in an external peripheral. Accordingly, in
at least some embodiments, one or more of the sensors in the
sleeping environment sensing array 306 may be provided in the
mattress 101 and, in at least some embodiments, one or more of the
sensors in the sleeping environment sensing array 306 may be
provided in the external peripheral.
[0143] As will be described in greater detail below, this
information could be used to provide reports to an occupant (e.g.
via a display, such as the display 390 of the sleep system or a
display 1290 (FIG. 12) on another device such as a mobile device
1200 connected to the smart mattress). These reports may evaluate
the sleep environment (i.e. the area in the vicinity of the
mattress). By way of example, information about the lighting
levels, dust levels, gas levels (such as carbon monoxide levels or
natural gas levels), humidity levels, temperature levels and/or
ambient noise levels may be provided. Furthermore, in at least some
embodiments, data of various types may used to generate a sleep
environment score. The sleep environment score may be based on two
or more of the following factors: lighting levels, gas levels, dust
levels, humidity levels, temperature levels and/or ambient noise
levels.
[0144] Room humidity information may be obtained from a room
humidity sensor 330. The room humidity sensor 330 may be of the
type described above (i.e. the body humidity sensor 124). However,
in at least some embodiments, the room humidity sensor 330 may be
located away from a region of the mattress in which an occupant
typically sleeps, to prevent the humidity sensor from capturing
humidity information associated with the occupant. For example, in
some embodiments, the room humidity sensor 330 may be included in
an external peripheral which may connect to the sleep system 100
through either a wired or wireless connection. The room humidity
sensor 330 generates an electrical signal based on the amount of
humidity in the region of the humidity sensor 330. That is, the
electrical signal output by the humidity sensor includes humidity
information. This humidity information may be provided to a
processor such as the main processor 117 for analysis.
[0145] Room temperature information may be obtained from a room
temperature sensor 332. The room temperature sensor 332 may be of
the type described above with reference to the body temperature
sensor 122. However, in at least some embodiments, the room
temperature sensor 332 may be located away from a region of the
mattress in which an occupant typically sleeps, to prevent the
temperature sensor from capturing temperature information
associated with the occupant. For example, in some embodiments, the
room temperature sensor 332 may be included in the external
peripheral described above. The room temperature sensor 332
generates an electrical signal based on the temperature in the
region of the room temperature sensor 332. That is, the electrical
signal output by the temperature sensor includes temperature
information. This temperature information may be provided to a
processor such as the main processor 117 for analysis.
[0146] In some embodiments, the sleeping environment sensing array
306 may include a microphone 334. The microphone 334 may, for
example, be used to obtain sound information. As is known, the
microphone may convert sound waves into electrical energy
variations, which may be provided as an electrical signal to a
processor (this signal may be converted to a digital signal by an
ADC before input to the processor in some embodiments). This
electrical signal may be said to contain sound information. This
sound information may, for example, indicate the amount of ambient
noise in the room where the sleep system 100 is located. In at
least some embodiments, the microphone 334 may be located away from
a region of the mattress in which an occupant typically sleeps, to
minimize the effect of noise from the occupant (e.g. due to
movements, snoring, etc.) on the captured sound. That is, the
microphone may be separated from the occupant so that the captured
sound indicates sound caused by other sources of noise or sound,
apart from the occupant. In other embodiments, the microphone may
be located near the occupant to detect occupant-generated audio,
such as snoring, breathing, etc.
[0147] In at least some embodiments, the microphone 334 may be a
condenser microphone, which may also be referred to as a capacitor
microphone or an electrostatic microphone. By way of example, in
some embodiments, the microphone 334 may be a CMC-2742WBL-25L model
microphone manufactured by CUI Inc.
[0148] In some embodiments, the sleeping environment sensing array
306 includes a light sensor 336. The light sensor 336 includes a
light sensitive element which generates an electrical signal
responsive to received light. That is, the electrical signal
includes light information which indicates the amount of received
light received at the light sensor 336. Thus, the light information
indicates how light (or how dark) the room is. In at least some
embodiments, the light sensor is an Everlight Electronics' ambient
light sensor, such as an AS-PT243-3C/L177. The light sensor 336 may
sense light in the visible range. In at least some embodiments, the
light sensor 336 may sense light with a wavelength in the range of
390 to 700 nm. The light information generated by the light sensor
is provided to a processor (such as the main processor 117) as an
electrical signal. The light sensor 336 may not function if it is
obstructed. Accordingly, in at least some embodiments, the light
sensor 336 is not included in the mattress 101 where it might be
obscured by bedding, for example; instead, the light sensor 336 may
be included in the external peripheral.
[0149] In some embodiments, the sleeping environment sensing array
306 includes a dust sensor 338. The dust sensor 338 may be an
optical dust sensor and may include an emitting diode and a
photoresisitor. By way of example, in some embodiments, the dust
sensor 306 may be a model GP2Y1010AU0F dust sensor manufactured by
Sharp.TM.. In some embodiments, the dust sensor 338 may measure
dust concentrations in the range of 0 to 0.8 mg/m.sup.3. The dust
sensor 338 generates an electrical signal which indicates the
amount of dust in the vicinity of the dust sensor 338. The amount
of dust in the vicinity of the dust sensor 338 may be referred to
as dust information. This dust information may be provided to a
processor (such as the main processor 117) as an electrical
signal.
[0150] The dust sensor 338 may not function if it is obstructed.
Accordingly, in at least some embodiments, the dust sensor 338 is
not included in the mattress 101 where it might be obscured by
bedding, for example; instead, the dust sensor 338 may be included
in the external peripheral.
[0151] The sensors in the sleeping environment sensing array 306
are coupled to one or more processors, such as the main processor
117. In the embodiment illustrated, the sensors in the sleeping
environment sensing array 306 connect directly to the main
processor 117. However, in other embodiments, these sensors may not
connect directly to the main processor; one or more
microcontrollers may be connected between the sleeping environment
sensing array and the main processor.
[0152] In some embodiments (not shown), the sleep environment
sensing array 306 may be provided in whole or in part by a mobile
device 1200. More particularly, sensors on the mobile device 1200
could be used as the sleeping environment sensing array 306.
[0153] It will be appreciated that the sleep system 100 may include
components in addition to those described above, including, for
example, additional sensors and that the components described above
may be arranged in a different manner than that illustrated in FIG.
1, 2 or 3. For example, in some embodiments, the microphone (which
is illustrated as being included in the sleeping environment
sensing array 306 in FIG. 3) could be instead included in an
occupant sensing array (such as the left occupant sensing array 302
and/or the right occupant sensing array 304). Such a microphone
could, for example, be used to detect sounds associated with the
occupant, such as snoring, etc.
[0154] By way of further example, in at least some embodiments, the
sleep system 100 may include one or more input interfaces which are
not illustrated in FIG. 3. Such input interfaces may include a
keyboard, keypad, button, touchscreen, etc. The input interface(s)
may be connected to a processor (such as the main processor 117) to
allow the processor to receive input. The input interfaces may also
be referred to as input mechanisms or input devices, in some
embodiments.
[0155] Furthermore, the humidity sensor(s) 124 which are described
as being embedded into the mattress 101 could be used for other
purposes apart from sensing conditions associated with the
occupant. For example, they may also be used to detect mattress
health information. For example, they may be used to determine
whether the mattress is too wet, which could cause mould.
[0156] As will also be described in greater detail below, in at
least some embodiments, one or more of the force sensors 120a-120h
which are embedded into the mattress 101 may be used for evaluating
the health of the mattress. For example, the force sensors
120a-120h could be used to monitor usage of the mattress. Usage
information may be used to provide feedback via an output interface
380 about the health of the mattress. This feedback may, for
example, prompt a user to flip and/or rotate the mattress and/or
may suggest replacement of the mattress.
[0157] Additionally, in at least some embodiments, the sleep system
100 may include timing circuitry or timing components. The timing
circuitry or timing components may be used, for example, to track a
time of day and/or a date. Accordingly, in at least some
embodiments, such timing components may include a clock. This
information may be used in some of the methods described below. For
example, this information, together with information from the force
sensors 120a-120h may be used to determine the time when an
occupant went to bed and/or when the occupant woke up. The timing
circuitry or timing components may be provided on a processor such
as the main processor 117 in at least some embodiments.
[0158] Furthermore, it will be appreciated that at least some of
the components described above may be omitted in at least some
embodiments. For example, one or more sensors could be omitted. For
example, in some embodiments, sensors associated with one or more
of the occupant sensing arrays 302, 304 may be included but sensors
associated with the sleeping environment sensing array 306 may be
omitted. By way of further example, in other embodiments, sensors
associated with one or more of the sleeping environment sensing
arrays 306 could be included and the sensors associated with the
occupant sensing arrays 302, 304 omitted.
Sleep State Information Determination
[0159] In at least some embodiments, one or more of the processors
that are included in the sleep system 100 or in a server, system or
device that is coupled to the sleep system may be configured to
determine sleep state information for an occupant based on data
obtained from one or more of the force sensors. The one or more
processors may include, for example, the main processor 117, the
microprocessors 130a, 130b, a processor provided on an external
peripheral of the type described above, a processor 1217 on a
mobile device 1200 connected or connectable to the sleep system
100, a processor on a remote server connectable to the sleep system
100, and/or another processor associated with the sleep system
100.
[0160] More particularly, one or more memories associated with the
one or more processors may include processor-executable
instructions which, when executed, configure the processor to
perform one or more of the methods 400, 500, 600, 700, 800, 900
described below with reference to FIGS. 4 to 9. For example, in
some embodiments, memory 372 associated with the main processor 117
may include such processor-executable instructions to configure the
main processor 117 to perform one or more of the methods.
[0161] The methods 400, 500, 600, 700, 800, 900 described below
with reference to FIGS. 4 to 9 may be used to determine sleep state
information. As will be described in greater detail below with
reference to FIGS. 4 to 9, the sleep state information may include,
for example: sleep stage information which indicates a sleep stage
of an occupant and/or the times at which the occupant entered
and/or exited various sleep stages (see FIG. 5), awake and/or
asleep status information which indicates whether the occupant is
awake or asleep and/or the times at which the occupant fell asleep
and/or woke up (see FIG. 5), sleep onset latency information which
is a measure of the amount of time required by an occupant to fall
asleep (see FIG. 5), sleep position information which indicates a
sleeping position of the occupant and/or the times when the
occupant entered and exited various sleeping positions (see FIG.
8), and/or sleep disorder information (see FIG. 9). The sleep
disorder information may indicate whether an occupant is suffering
from a sleep disorder, the nature of the sleep disorder affecting
the occupant, and/or a likelihood score which indicates the
likelihood that the occupant is suffering from a given sleep
disorder.
[0162] At least some of the sleep state information described above
may be determined based on movement information which indicates the
quantity and or times of movements of an occupant (see FIG. 4),
heart rate information which indicates a heart rate of the occupant
and which may track changes in the heart rate over time (see FIG.
6), and/or respiration rate information which indicates a
respiration rate of the occupant and which may track changes in the
respiration rate over time (see FIG. 7).
[0163] After sleep state information is determined by a processor
associated with the sleep system, it may be store in memory (such
as the memory 372 associated with the main processor 117 and/or
memory associated with a mobile device 1200 wirelessly connected to
the sleep system 100 and/or memory associated with a server
connected to the sleep system 100 and/or the mobile device 1200)
and/or may be used to generate an output at an output interface
associated with the sleep system or a mobile device connected or
connectable to the sleep system. In some embodiments, the output
interface may be a display. For example, in some embodiments, an
alarm may be generated on the display based on the sleep state
information. By way of example, the alarm may inform a user that
they are likely suffering from a sleep disorder.
Extraction of Movement Component
[0164] In at least some embodiments, the sleep system 100 may
extract a movement component from the data obtained from the force
sensors. This extraction may, for example, obtain a movement
component which represents movements of the occupant which are not
caused by heart or breathing induced movements. That is, the
movement component may represent movements that are caused by an
occupant shifting in bed, changing positions in bed, moving a limb,
etc.
[0165] In some embodiments, the sleep system 100 may determine
whether a given sample obtained from a force sensor 120a-120h
represents movement of the occupant. In at least some embodiments,
this determination may be performed based on changes of force over
time using a moving average difference method. That is, sudden
changes of force measured at one of the force sensors may be
interpreted as a movement.
[0166] Referring now to FIG. 4, one example method 400 will now be
discussed. The method 400 may, for example, be performed by one or
more processors connected to or associated with the sleep system
100, such as the main processor 117 and the microprocessors 130a,
130b. More particularly, one or more memories associated with the
one or more processors may include processor-executable
instructions which, when executed, configure that processor to
perform a method described below.
[0167] At 402, sensor data is obtained from the force sensor(s)
120a-120h. The sensor data may be periodically obtained; for
example, at a predetermined interval and, in at least some
embodiments, the sensor data may be obtained from each of the force
sensors 120a-120h. The sensor data obtained at 402 may represent
readings at discrete points in time which may be referred to as
samples. Each sample is, therefore, associated with a specific
point in time. In at least some embodiments, samples from all of
the force sensor(s) may be obtained at each time interval. That is,
all of the force sensors may be sampled at once to obtain a number
of samples representing the force measured at various locations of
the mattress 101 at a single point in time. The sensor data (i.e.
the samples) may, in at least some embodiments, be stored at 402 in
a memory associated with the sleep system 100, such as memory 372
associated with the main processor 117. In other embodiments, the
sensor data may be stored in another type of memory, such as a
cache.
[0168] At 404, the processor determines whether a movement occurred
based on the sensor data obtained from the force sensor(s). In at
least some embodiments, the processor may determine whether a
movement has occurred at a given time, t1, by comparing the sensor
data (i.e. the force reading) from a force sensor at that point in
time to sensor data from that same force sensor before and/or after
that given time (i.e. before or after t1).
[0169] In some embodiments, the processor may determine whether a
movement has occurred at a given time by comparing front window
readings to back window readings. The front window readings are
sensor samples obtained before the given time for which movement is
being evaluated and the back window readings are sensor samples
obtained after the given time for which movement is being
evaluated. In at least some embodiments, the processor may
determine whether a movement has occurred at a given time by
comparing an average of a predetermined number of front window
readings with an average of a predetermined number of back window
readings. That is, a moving average difference method may be used
to determine whether a movement has occurred at a given time. This
moving average difference may, in some embodiments, be a
multi-point average difference method which calculates the average
of multiple samples in the front window and multiple samples in the
back window in order to determine whether a movement has occurred.
By way of example, in some embodiments, a five point average
difference method may be used which calculates the average of five
samples in the front window and five samples in the back
window.
[0170] By way of example, in some embodiments, the processor may
determine whether a movement has occurred at a given time, t1, by
evaluating the following equation to find a difference, D, between
a front window average and a back window average for a sensor,
k:
D = i = n [ k ] - x n [ k ] - 1 i x - i = n [ k ] + 1 n [ k ] + x i
x ##EQU00001##
where k is used to identify the specific force sensor from which
the data is obtained, n[k] is the current point of data (i.e. the
data at time t1) from the force sensor, x is a predetermined number
of samples which will be used to form each of the front window and
the back window. In at least some embodiments, the predetermined
number, x, is five.
[0171] After obtaining the difference, D, the processor may compare
the difference to one or more predetermined thresholds. In at least
some embodiments, if the difference, D, is above the predetermined
threshold, then a movement is determined to have occurred. In other
embodiments, a movement may be detected based on other criteria.
For example, in some embodiments, the rate of change of the
difference, D, and/or the magnitude of the difference, D, may be
compared to respective thresholds to determine whether a movement
has occurred. In at least some embodiments, a movement may also be
categorized, by a processor, in terms of the speed and size of the
movement.
[0172] This determination may be performed separately for each
force sensor 120a-120h in a sensor set 150, 152. That is, the
processor may analyze sensor data from each force sensor 120a-120h
independently to determine whether a movement has occurred. For
example, in some embodiments, the difference, D, between the front
window average and the back window average may be evaluated for
each force sensor 120a-120h and each of these differences may be
compared with one or more predetermined thresholds. If any of the
differences for the force sensors 120a-120h in a sensor set 150
associated with a first occupant exceed the respective threshold,
then the processor may determine that the first occupant has moved
at the time t1. That is, if any of the force sensors 120a-120h
which are in the first sensor set 150 indicate that a movement has
occurred, then the processor may determine that the occupant
associated with the first sensor sent 150 has moved. This
determination may be recorded in memory at 406. That is, movement
information may be stored in memory at 406, which may be memory
associated with the sleep system 100. For example, the processor
may update the memory to, for example, increment a movement counter
associated with the first sensor set 150 to indicate that the
occupant associated with the first sensor set 150 has moved. By way
of further example, in some embodiments, after determining that a
movement has occurred, the processor may update the memory to
indicate a time associated with the movement.
[0173] If, however, none of the differences for the force sensors
120a-120h in the sensor set 150 exceed the respective threshold
(i.e. if none of the force sensors 120a-120h in the sensor set 150
indicate that a movement has occurred at time t1), then the
processor determines that no movement of the occupant associated
with that sensor set 150 has occurred at the given time, t1. In
some embodiments, at 406 the processor may update memory to store
movement information which indicates that a movement did not occur
at time t1.
[0174] The movement determination described above may be performed
independently for each sensor set 150, 152. That is, sensor data
from the force sensors 120a-120h associated with the first sensor
set 150 may be used to determine whether a first occupant has moved
and sensor data from the force sensors 120a-120h associated with
the second sensor set 150 may be used to determine whether a second
occupant has moved.
[0175] Additionally, while the method 400 described above generally
refers to a determination of movement at a single point in time, in
practice, the steps of the method may be repeated to determine
whether a movement has occurred over the course of an extended
period of time. For example, in some embodiments, a determination
as to whether a movement has occurred may be made for each sensor
sample.
[0176] Furthermore, since a single movement may create a change in
the force reported at a force sensor for an extended period of
time, to prevent double recording of movements, in at least some
embodiments, the processor may be configured to enforce one or more
rules regarding the maximum number of movements that will be
counted for each sensor set 150, 152 within a given time frame. For
example, in at least some embodiments, the processor may only
permit one movement to be registered for each sensor set 150, 152
each second. In such embodiments, when a movement is detected at
one of the sensor sets 150, 152, the processor may wait until the
predetermined period of time (e.g. one second) has expired before
it will permit another movement to be registered.
[0177] Thus, the method 400 may be used to identify movements of an
occupant from data obtained from the force sensors 120a-120h.
Determine Sleep Stage and/or Whether Occupant is Awake
[0178] As noted above, in at least some embodiments, sleep state
information may be determined by a processor based on data obtained
from the force sensors. This sleep state information is information
about an occupant's sleep. In some embodiments, this sleep state
information may indicate whether an occupant is asleep. In some
embodiments, this sleep state information may indicate the sleep
stage of the occupant.
[0179] Referring now to FIG. 5, an example method 500 for
determining such sleep state information is illustrated. The method
500 may be used, for example, to determine a sleep stage of the
occupant of a mattress 101 and/or to determine whether an occupant
of the mattress 101 is either asleep or awake.
[0180] In at least some embodiments, the method 500 may include or
be performed after the method 400 of FIG. 4. That is, during the
method 500 of FIG. 5, the processor may use movement information to
determine a sleep stage of an occupant and/or to determine whether
the occupant is asleep or awake. That is, based on the frequency of
movements of an occupant, the sleep stage and/or waking status of
that occupant may be determined.
[0181] Accordingly, in at least some embodiments, the method 400 of
FIG. 4 may be performed to obtain movement information. As noted in
the discussion of FIG. 4 above, during the performance of this
method 400, movements of an occupant are identified from data
obtained from the force sensors. The steps 402, 404, 406 of the
method 400 are described above with reference to FIG. 4.
[0182] At 502, the processor determines a frequency of movements.
More specifically, the processor determines the amount of movements
for an occupant that have occurred within an epoch of a
predetermined duration. That is, the processor may determine the
amount of movements that have occurred within a predetermined
period of time. By way of example, in some embodiments, this period
of time may be one minute. In some embodiments, this period of time
may be in the range of thirty seconds to one minute. Other ranges
are possible in other embodiments.
[0183] The determination of the frequency of movements at 502 is
performed based on the movement information obtained during the
method 400.
[0184] Then, at 504, the processor determines a sleep stage
associated with the occupant and/or whether the occupant is awake
or sleeping. This determination may be made, for example, based on
the frequency of movements determined at 502. More particularly,
the processor may determine the sleep stage of the occupant by
comparing the amount of movements of the occupant within the epoch
to one or more predetermined thresholds. The sleep stages may be
the stages accepted by the American Academy of Sleep Medicine.
[0185] Similarly, in at least some embodiments, the processor may
determine the waking status (i.e. whether the occupant is awake or
asleep) by comparing the amount of movements of the occupant within
the epoch to one or more predetermined thresholds.
[0186] In at least some embodiments, in determining a sleep stage
which an occupant is in during a given epoch and/or in determining
a waking status, the processor may either determine: 1) that the
occupant is awake; 2) that the occupant is in a non-rapid eye
movement (NREM) stage 1 state; 3) that the occupant is in a NREM
stage 2 state; 4) that the occupant is in a NREM stage 3 state; or
5) that the occupant is in a rapid eye movement (REM) state. These
various states and the respective thresholds associated with these
states will now be described.
[0187] An awake state occurs when the occupant is not sleeping.
During this state, the occupant's movement tends to have a higher
relative frequency than other states. Accordingly, the processor
may determine that the occupant was in a waking state during an
epoch if the measure of movements of the occupant during the epoch
exceeds a first predetermined threshold. The first predetermined
threshold is relatively higher than the thresholds associated with
the other states described below.
[0188] The NREM stage 1 state is a sleep stage which is between
sleep and wakefulness. An occupant's muscles are active during this
state and the movement of the occupant tends to be more frequent
than in the REM, NREM stage 2, and NREM stage 3 states. The amount
of movement is, however, typically less than in the waking state.
Accordingly, the processor may determine that the occupant was in
the NREM stage 1 state during the epoch if the measure of movements
of the occupant during the epoch exceeds a second predetermined
threshold and is less than the first predetermined threshold
associated with the waking state. The second predetermined
threshold is relatively lower than the first predetermined
threshold but is relatively higher than the thresholds associated
with the REM, NREM stage 2, and NREM stage 3 states.
[0189] REM sleep occurs when most muscles are paralyzed. Thus, the
frequency of movements during REM sleep tends to be less than in
the waking state and less than in the NREM stage 1 state, but more
than in the NREM stage 2, and NREM stage 3 states. Accordingly, the
processor may determine that the occupant was in the REM state
during the epoch if the measure of movements of the occupant during
the epoch exceeds a third predetermined threshold and is less than
the second predetermined threshold associated with the NREM stage 1
state. The third predetermined threshold is relatively lower than
the first predetermined threshold and the second predetermined
threshold but is relatively higher than the thresholds associated
with the NREM stage 2, and NREM stage 3 states.
[0190] NREM stage 2 sleep is a period of theta activity, where it
is difficult to awaken the occupant. NREM stage 2 sleep is
typically characterized by less frequent movements than the waking,
NREM stage 1 and REM states, but more frequent movements than in
the NREM stage 3 state. Accordingly, the processor may determine
that the occupant was in the NREM stage 2 state during the epoch if
the measure of movements of the occupant during the epoch exceeds a
fourth predetermined threshold and is less than the third
predetermined threshold associated with the REM state. The fourth
predetermined threshold is relatively lower than the first, second
and third predetermined thresholds.
[0191] NREM stage 3 is a slow wave sleep (SWS) stage. During this
stage, the occupant is less responsive to the environment. This
stage was formerly divided into two stages--3 and 4. Accordingly,
the NREM stage 3 state may be referred to or separated into NREM
stage 3 and NREM stage 4 states in some embodiments. NREM stage 3
sleep is typically characterized by less frequent movements than in
the other sleep states referred to above. Accordingly, the
processor may determine that the occupant was in the NREM stage 3
state during the epoch if the measure of movements of the occupant
during the epoch is less than the fourth predetermined
threshold.
[0192] Accordingly, in at least some embodiments, four
predetermined thresholds may be used to determine which of the five
sleep states discussed above an occupant is in during the epoch. It
will be appreciated that a different number of thresholds may be
used in other embodiments. For example, in some embodiments, the
processor may be configured to determine whether the occupant is
either in: 1) an asleep state; or 2) an awake state. An asleep
state may be a state in which the occupant is either in the REM,
NREM stage 2 or NREM stage 3 state. In some embodiments, the asleep
state may also include the NREM stage 1 state. That is, if an
occupant is in NREM stage 1, then they may be considered to be
asleep. In some embodiments, such relationships may be used to
determine that an occupant is asleep; for example, if the user is
in either the REM, NREM stage 2, NREM stage 3 (and in some
embodiments NREM stage 1) states, then the processor may determine
that the occupant is asleep. However, in other embodiments, the
determination of whether an occupant is asleep or awake may be
performed in another manner. For example, a single threshold may be
used in some embodiments. That is, the measure of movements of an
occupant during an epoch may be compared to this threshold, and if
the movements exceed the threshold then the occupant may be
determined to be awake, but if the movements do not exceed the
threshold then the occupant may be determined to be asleep.
[0193] Accordingly, in at least some embodiments, at 504, the
processor may determine sleep state information which indicates
whether the occupant is asleep during an epoch and/or a stage of
sleep which the occupant was in during the epoch. Such sleep state
information may be stored in memory associated with the sleep
system 100, output to a display associated with the sleep system
100 or an associated device or system (such as a mobile device),
etc. For example, in some embodiments, a sleep log may be updated
and/or created. The log may indicate the time at which a user fell
asleep, woke up, entered each stage of sleep and/or exited each
stage of sleep.
[0194] The method 500 may be repeatedly performed to track such
information over a prolonged period of time; for example,
throughout the night.
[0195] The method 500 may be independently performed for each
occupant. That is, for each sensor set 150, 152 that is associated
with a different occupant, the method 500 may be independently
performed so that, for each occupant, the processor independently
determines the sleep stage which that occupant is in and/or whether
that occupant is asleep.
[0196] In some embodiments, other information may be used instead
of or in addition to the movement information described above to
predict the sleep stage of an occupant. For example, in some
embodiments, body temperature, heart rate and/or respiration rate
may be used to predict the sleep stage of the occupant.
Accordingly, the processor may be configured to determine the sleep
state information based on temperature readings, heart rate,
respiration rate, and/or other information, in some
embodiments.
[0197] In some embodiments, at 506 an alarm associated with an
alarm clock function may be triggered based on the sleep stage of
the occupant. More particularly, an input interface provided on the
sleep system 100 or on a device connected to the sleep system (such
as a mobile device) may be used to allow an occupant to input
timing information associated with the alarm. The timing
information may, for example, indicate an ideal time when the user
would prefer to wake up, a latest time when the user would like to
wake up and/or a range of times during which the user would like to
wake up. A wakeup window may be determined from such information by
the processor. The wakeup window is the range of times during which
an alarm will be triggered to wake up the occupant. The processor
then uses the sleep stage information to predict the time during
the wakeup window when the occupant will be in the lightest stage
of sleep. An alarm may then be triggered at the predicted time. The
alarm may, for example, be an audible, visual and/or vibratory
alarm which may be produced through an output interface of the
sleep system 100, such as a speaker or vibratory device (such as a
vibration motor which may be embedded into the mattress on one of
the sides of the mattress and which could be used for waking one
occupant but not the other occupant i.e. it may be located at or
near one side but away from the other side and each side may have a
separate vibration motor, each associated with a separate one of
the occupants), or through an output interface of a connected
peripheral or device, such as a mobile device.
[0198] As noted above, in some embodiments, the mattress 101 may be
configured for two occupants. In such embodiments, the sleep state
of both occupants may be used by the processor when selecting a
time for triggering the alarm during the wakeup window. For
example, in some embodiments, the processor may determine a time
when the occupants will collectively be in their lightest sleeps
states. By way of example, this determination may be made by
assigning scores to each of the sleep stages, with the lowest score
representing the lightest stage of sleep and the highest the
deepest sleep. A joint sleep score could be defined as the sleep
score of all occupants of the mattress 101. Then, the processor may
select a time for triggering the alarm by finding the time within
the wakeup window that minimizes the joint sleep score.
[0199] Alternatively, in some embodiments where the mattress 101 is
configured for use by two occupants, one of the occupants may be
selected by the processor for the purposes of triggering the alarm.
For example, in some embodiments, one of the occupants may be
selected by determining which of the occupants had a worse sleep.
In some embodiments, the alarm may then be triggered based on the
sleep stage of the occupant having the relatively worse sleep. The
occupant having a relatively worse sleep may be the occupant who:
slept the least, woke up the most, had a lower sleep score, etc.
Example methods of determining a sleep score for an occupant will
be discussed in greater detail below.
[0200] In some embodiments, the alarm, once triggered may be shut
off when the processor detects that one of the occupants gets off
the bed. In some embodiments, the alarm, once triggered may be shut
off when the processor detects that all of the occupants got off
the bed. The processor may determine whether an occupant has gotten
off the bed based on data obtained from the force sensors. For
example, when the force sensors 120a-120h indicate forces below one
or more thresholds, then the processor may determine that the
occupant has gotten off the bed and may stop the alarm.
[0201] In some embodiments, the force sensors 120a-120h may also be
used as an input interface which allows an occupant of the mattress
101 to input an instruction to the processor to instruct the
processor to enable a snooze function of the alarm (or to input
another instruction). For example, the processor may be configured
to recognize one or more gestures which may be performed by
movement of the occupant's body and which may be detected using
data from one or more of the force sensors 120a-120h. By way of
example, one possible gesture may involve a user briefly lifting
one or more limbs (such as a leg) and then forcefully placing that
limb back onto the mattress. Such a gesture may, for example, be
interpreted as a snooze command.
[0202] The sleep stage information which is determined according to
the method 500 of FIG. 5 may have other uses instead of or in
addition to the alarm. For example, in some embodiments, at 508,
sleep onset latency may be determined. The sleep onset latency is a
measure of the difference between the time when an occupant
attempted to fall asleep and the time when that occupant fell
asleep (which may be determined at step 504). The time when the
occupant attempted to fall asleep may be determined before 508 and
this step is not specifically illustrated in FIG. 5. By way of
example, it may be determined after step 402 of FIG. 5.
[0203] The time when an occupant attempted to fall asleep is, in at
least some embodiments, the time when the occupant went to bed. The
time when an occupant went to bed is the time when the occupant
laid on the mattress after having previously not been on the
mattress. This time may be identified by the processor based on
data from the force sensors 120a-120h. That is, when an occupant
goes to bed (i.e. lays on the mattress 101), the processor
identifies a large increase in the force measured on at least some
of the force sensors (i.e. it detects presence of the occupant).
Thus, the processor may determine that an occupant enters the bed
when the force measured at a predetermined number of the force
sensors 120a-120h exceeds a predetermined threshold. In at least
some embodiments, the force sensors 120a-120h may be calibrated so
that when the sleep system 100 has no occupants, the force readings
from each of the force sensors 120a-120h may be zero.
[0204] In some embodiments, a further check may be performed to
confirm that the change in force was due to an occupant entering
the mattress and not, for example, due to an object being placed on
the mattress. For example, a temperature may be obtained from a
temperature sensor 122 and compared to a threshold to determine
that an occupant has entered the mattress. Furthermore, in at least
some embodiments, the processor may require that at least a
predetermined number of force sensors are engaged (e.g. are
registering forces which exceed one or more thresholds) and/or may
require that specific force sensors are engaged before determining
that an occupant has entered the mattress. For example, if an upper
body force sensor registers a force which exceeds a predetermined
threshold, but a middle body force sensor does not register a force
which exceeds a predetermined threshold, then the processor may
determine that the occupant has not yet entered the bed; the force
registered at the upper body force sensor may be caused by an
object apart from a human occupant.
[0205] In some embodiments, to determine the time when the occupant
attempted to fall asleep, the processor may also consult data from
the light sensor 336 (FIG. 3). As noted in the discussion of FIG. 3
above, in some embodiments, the sleep system 100 may include or be
associated with a light sensor 336. In some such embodiments, this
light sensor 336 may be used to identify the time when a user
attempted to fall asleep. That is, in some embodiments, the
processor may determine that an occupant has attempted to fall
asleep when at least the following two conditions are satisfied: 1)
the user has entered the mattress (methods for determining whether
the occupant has entered the mattress are described immediately
above); and 2) the light measured at the light sensor 336 is less
than a predetermined threshold. The predetermined threshold may,
for example, be a threshold which indicates that the main source of
artificial lighting in the room containing the mattress has been
turned off or that all sources of artificial lighting are turned
off.
[0206] After determining that an occupant has attempted to fall
asleep and/or entered the mattress, the processor may store, in
memory associated with the sleep system, timing information to
indicate the time when the occupant first entered the mattress
and/or first attempted to fall asleep. This timing information may
then be retrieved at 508 and used to determine sleep onset latency.
More particularly, the difference between the time when the
occupant fell asleep (as determined at 504) and the time when that
occupant attempted to fall asleep may be determined, and this
elapsed time is the sleep onset latency.
[0207] The determination of the sleep onset latency may be
performed independently for each occupant of the mattress 101.
[0208] The sleep onset latency, which is a further type of sleep
state information, may be stored in memory of the sleep system 100.
The sleep onset information may, in at least some embodiments, be
used to determine a sleep score associated with an occupant and/or
to determine whether the occupant suffers from a sleep disorder,
such as insomnia. Techniques for determining a sleep score and
detecting sleep disorders are described below.
[0209] In at least some embodiments, a sleep offset latency (which
may also be referred to as wake latency) may be determined by the
processor associated with the sleep system 100 or an associated
device. This may, in some embodiments, be performed at 508 of FIG.
5. The sleep offset latency is a measure of the amount of time an
occupant remains in bed after they wake up. For example, the
processor may determine the elapsed time between when the occupant
woke up (e.g. when they are no longer in one of the sleep stages in
which they are considered to be "asleep") and when the occupant got
out of bed (which may be determined from the force sensors and/or
the light sensor 336 (e.g. if a light is turned on, in some
embodiments, the occupant may be considered to have gotten out of
bed since the occupant is no longer actively trying to sleep).
Heart Rate Determination
[0210] Due to the principle of ballistocardiography, the pumping of
the heart causes oscillatory body motion and mechanical forces to
be produced. This force can be measured using the force sensors
120a-120h over time and a heart rate determined.
[0211] Referring now to FIG. 6, one such example method 600 is
illustrated.
[0212] At 602, data is obtained from one or more of the force
sensor(s) and may be stored in memory. This feature may, for
example, be performed together with step 402 of FIGS. 4 and 5 and
may be performed in the manner described with reference to step
402. Since heart rate is typically between 0.5 to 4 Hz, the data
may be obtained at 602 at a frequency that is greater than 4 Hz.
For example, in at least some embodiments, samples may be obtained
at 602 at a rate of 10 Hz.
[0213] At 604, the processor determines, from the data obtained
from the one or more force sensors, a heart rate for an occupant.
The heart rate may, for example, be determined based on data from
the upper body force sensors, which are described above with
reference to FIG. 1. More specifically, in at least some
embodiments the lower body force sensors are not used for the
determination of the heart rate. Furthermore, in at least some
embodiments, the middle body force sensors are not used for the
determination of the heart rate.
[0214] To determine the heart rate (at 604), the processor may
filter out large changes in force measured at the force sensors
120a-120h which are caused by movement of an occupant. Voluntary
body movement typically occurs in the frequency range of 0.25-4 Hz,
which overlaps with the heart rate frequency range, so these
signals must be discriminated. Changes in force measured at the
force sensors that are caused when an occupant shifts positions
tend to be greater in magnitude than the changes caused by the
occupant's breathing or heart activity. This filtering may be done
by comparing the change in force to one or more predetermined
thresholds. The processor may also perform smoothing on the data
obtained at 602, and may filter out lower frequency components,
such as a component caused by respiration or movement, which will
be described in greater detail below. Filtering of the frequency to
remove frequencies outside of the range of the heart rate (0.5-4
Hz) may be done using linear cut-off filters or bandpass filters
designed based on Window functions. Furthermore, the data may be
smoothed, amplified, or otherwise processed to obtain a high
quality heart rate signal. The heart rate can be extracted using a
variety of techniques that can detect the peaks in the data, which
can be used to find the interpeak separation and hence the heart
rate. Peak detection can be done in a variety of ways such as
detection of local minima or maxima in a moving window or by using
a fast fourier transform (FFT) and examining the harmonics. The
heart rate may be determined at predetermined intervals to obtain
heart rate information for an extended period of time and to
monitor for changes in the heart rate.
[0215] In at least some embodiments, the heart rate may be stored
in memory at 606. The heart rate may, for example, be used to
determine sleep state information for the occupant. For example,
the heart rate may be used to determine a sleep stage of the
occupant. The heart rate may, in some embodiments, be used by a
processor associated with the sleep system or an associated device
for evaluating other health related issues. For example, in some
embodiments, a heart rate variability (HRV) may be determined by
the processor. This HRV may be stored in memory. In some
embodiments, the HRV may be used by a processor to detect other
conditions. For example, a lower than normal HRV may be indicative
of heart failure, diabetic neuropath, depression, post-traumatic
stress disorder (PTSD), stress, susceptibility to sudden infant
death syndrome, etc. HRV can also be related to having sleep apnea.
Thus, in at least some embodiments, the HRV may be compared, by a
processor, to one or more predetermined thresholds to determine
whether an occupant has, is likely to have and/or is susceptible to
any one or more of these conditions. Faster resting heart is a risk
factor for cardiovascular mortality and can be an indicator of a
heart attack. It may also be used to detect arrhythmias and other
heart rate abnormalities. Accordingly, in some embodiments, a
processor may use the heart rate to determine whether an occupant
has, is likely to have and/or is susceptible to: cardiovascular
mortality, heart attacks, arrhythmias, and/or heart rate
abnormalities.
[0216] It will be appreciated that the method 600 may be performed
independently for each occupant of the mattress. For example, the
force sensors in the first sensor set 150 may be used to determine
the heart rate of a first occupant and the force sensors in the
second sensor set 152 may be used to determine the heart rate of a
second occupant.
Respiration Rate Determination
[0217] In at least some embodiments, the processor may be
configured to determine a respiration rate of the occupant based on
data obtained from the force sensors. Referring now to FIG. 7, one
such example method 700 is illustrated.
[0218] At 702, data is obtained from one or more of the force
sensor(s) and may be stored in memory. This feature may, for
example, be performed together with step 402 of FIGS. 4 and 5
and/or step 602 of FIG. 6 and may be performed in the manner
described with reference to step 402. Since respiration rate is
typically between 0.1 to 0.5 Hz, the data may be obtained at 702 at
a rate that is greater than 0.5 Hz. For example, in at least some
embodiments, samples may be obtained at 702 at a rate of 10 Hz.
[0219] At 704, the processor determines, from the data obtained
from the one or more force sensors, a respiration rate for an
occupant. The respiration rate may, for example, be determined
based on data from the upper body force sensors, which are
described above with reference to FIG. 1. More specifically, in at
least some embodiments the lower body force sensors are not used
for the determination of the respiration rate.
[0220] To determine the respiration rate (at 704), the processor
may filter out large changes in force measured at the force sensors
120a-120h which are caused by movement of an occupant. Voluntary
body movement typically occurs in the frequency range of 0.25-4 Hz,
which may overlap with the respiration rate frequency range, so
these signals are discriminated. Changes that are caused when an
occupant shifts positions tend to be greater in magnitude than the
changes caused by the occupant's breathing or heart activity. This
filtering may be done by comparing the change in force to one or
more predetermined thresholds. The processor may also perform
smoothing on the data obtained at 702, and may filter out higher
frequency components, such as movement components and may, in some
embodiments, filter out higher frequency components, such as a
component caused by heart activity. Filtering of the frequency to
remove frequencies outside of the range of the respiration rate
(0.1-0.5 Hz) may be done using linear cut-off filters or bandpass
filters designed based on Window functions. As noted above,
respiration rate is typically in the range of 0.1-0.5 Hz and heart
rate is typically in the range of 0.5-4 Hz. These ranges may be
used to separate the respiration component from the heart rate
component. For example, one or more thresholds may be established
based on these ranges to separate the heart rate component from the
respiration component. Furthermore, the data may be smoothed,
amplified, or otherwise processed to obtain a high quality
respiration rate signal. The respiration rate can be extracted
using a variety of techniques that can detect the peaks in the
data, which can be used to find the interpeak separation and hence
the respiration rate. Peak detection can be done in a variety of
ways such as detection of local minima or maxima in a moving window
or by using a fast fourier transform (FFT) and examining the
harmonics. The respiration rate may be determined at predetermined
intervals to obtain respiration rate information for an extended
period of time and to monitor for changes in the respiration
rate.
[0221] In at least some embodiments, the respiration rate may be
stored in memory at 706. The respiration rate may, for example, be
used to determine sleep state information for the occupant. For
example, as will be described in greater detail below, the
respiration rate may be used to determine whether the occupant has
a sleep disorder. For example, in some embodiments, a processor may
consider the respiration rate when determining whether an occupant
has sleep apnea.
[0222] It will be appreciated that the method 700 may be performed
independently for each occupant of the mattress. For example, the
force sensors in the first sensor set 150 may be used to determine
the respiration rate of a first occupant and the force sensors in
the second sensor set 152 may be used to determine the respiration
rate of a second occupant.
Sleep Position Monitoring
[0223] In at least some embodiments, the processor may be
configured to determine sleep state information which identifies a
sleep position of an occupant of the mattress 101 (such information
may also be referred to as sleep position information). In at least
some embodiments, the processor may be configured to determine the
most common sleep position of the occupant.
[0224] In at least some embodiments, the processor may be
configured to recognize predetermined common sleep positions. In
some embodiments, these positions may include: a fetus position, a
freefall position, a log position, a yearner position, a solider
position and a starfish position. The characteristics of these
positions are described below. The processor may be configured to
identify other positions instead of or in addition to these
positions in other embodiments.
[0225] In the fetus position, the occupant sleeps on their side in
a curled up position. At least one of the occupant's hands is
resting near their chin. The fetus is the most common sleep
position. More particularly, approximately 41% of people sleep in
the fetus position. Thus, the probability that a given occupant
will prefer the fetus position is approximately 41%.
[0226] The freefall position is a position in which the occupant
lies on their stomach with their hands typically elevated, so that
they are near the occupant's head. The occupant's head is typically
turned to one side. Approximately 7% of people sleep in the
freefall position, and so the probability that a given occupant
will prefer the freefall position is approximately 7%.
[0227] The log position is characterized by the occupant lying on
their side with both arms down by their side. The back and legs of
the occupant are generally straight in the log position.
Approximately 15% of people sleep in the log position, and so the
probability that a given occupant will prefer the log position is
approximately 15%.
[0228] The yearner position is a position in which the occupant
sleeps on their side with both arms extended in front of them (i.e.
the arms are not at the side of the occupant's body but instead
extend in a direction which is generally perpendicular to the
occupant's torso). Approximately 13% of people sleep in the yearner
position and so the probability that a given occupant will prefer
the yearner position is 13%.
[0229] The soldier position is a position in which the occupant
lies on their back with both arms at their sides. That is, the arms
are generally parallel to the torso and typically rest on the
mattress. Approximately 8% of people sleep in the soldier position
and so the probability that a given occupant will prefer the
soldier position is 8%.
[0230] The starfish position is a position in which the occupant
lies on their back with both arms up around their pillow. That is,
the occupant's hands are generally near their head. Approximately
5% of people are said to sleep in the soldier position and so the
probability that a given occupant will prefer the starfish position
is 5%.
[0231] The various sleep positions described above may create
different force distributions across the force sensors 120a-120h.
Thus, the sleep position of an occupant may be determined, by the
processor, by examining the distribution of forces across the force
sensors.
[0232] More particularly, memory 372 associated with the processor
117 may store characteristic information associated with each of a
plurality of sleep positions which the processor is configured to
identify. This characteristic information may represent a force
distribution pattern for each position. Referring to FIG. 8, which
illustrates a method 800 of determining sleep position (i.e.
determining "sleep position information"), the processor may obtain
data at 802 from the force sensors 120a-120h in the manner
described above with reference to step 402 of FIG. 4. This data may
be used at 804 to determine the sleep position represented by the
data. More particularly, the processor may compare the data
obtained from the force sensor(s) with the characteristic
information to determine the sleep position associated with the
occupant. That is, the processor may determine which one of a
plurality of predetermined common sleep positions are represented
by the sensor data obtained from the force sensors in a sensor set.
This determination may be performed independently for each sensor
set so that the sleep position of each occupant may be separately
determined.
[0233] In at least some embodiments, in determining the sleep
position, the processor may consider other data in addition to the
force distributions represented in the sensor data. For example,
the processor may consider the relative probabilities of each sleep
position occurring for an occupant. As noted above, certain sleep
positions are more common than others in the general population.
This information (i.e. the probability of a random occupant using
each sleep position) may, in some embodiments, be considered by the
processor when determining the sleep position. For example, in some
embodiments, where the distribution of forces does not clearly
suggest a specific position (i.e. where the result of this analysis
suggests that the occupant may be in one of at least two
positions), then the probability information may be used to resolve
the ambiguity. For example, the freefall position and the starfish
position may produce similar force distributions. Thus, in some
circumstances, the force distribution analysis may suggest that the
occupant is either in the freefall position or the starfish
position, but the force distribution analysis may not clearly
indicate which of these two positions are being used. In some
embodiments, the processor may resolve this ambiguity by
determining that the freefall position is being used, since this
position is more common in the general population.
[0234] The sleep position of a user may be stored, as sleep state
information, in memory associated with the sleep system 100 at
806.
[0235] The sleep position of the occupant may be determined
repeatedly to account for changes in the occupant's sleep position.
In some embodiments, the sleep position may be determined
periodically. In some embodiments, the sleep position may be
re-determined in response to changes in force distributions
observed at the force sensors 120a-120h. Other triggers may be used
to cause the sleep position to be re-determined in other
embodiments.
[0236] In some embodiments, timing information may be associated
with the determined sleep position. That is, the processor may
record, in memory, a time at which an occupant entered and/or
exited a sleep position. In some embodiments, this timing
information may be used to determine an occupant's most common
sleep position over an extended period of time, such as a week, a
month, a year, etc.
[0237] In at least some embodiments, after the most common sleep
position has been determined, it may be recorded in memory as sleep
state information. In some embodiments, this sleep position may be
output via an output interface associated with the sleep system
100. For example, in some embodiments, the sleep position may be
displayed on a display associated with the sleep system. In some
embodiments, the display may be provided on the sleep system itself
and in other embodiments, the display may be provided on a mobile
device 1200 which is connected to the sleep system.
Detection of Sleep Disorder(s)
[0238] In some embodiments, one or more of the processors
associated with the sleep system 100 may be configured to detect
one or more sleep disorders. A detected sleep disorder may, for
example, be a type of sleep state information that is determined by
the sleep system 100.
[0239] Referring now to FIG. 9, an example of a method 900 for
detecting a sleep disorder is illustrated. At 902, data is obtained
from one or more of the sensors associated of the sleep system 100.
For example, data may be obtained from the force sensors 120a-120h,
the temperature sensor(s) 122, the humidity sensor(s) 122, the
microphone 334, or any of the other sensors described above with
reference to FIGS. 1 and 3. As will be understood from the
discussion of the various sleep disorders below, the specific
sensors from which data will be obtained will depend on the
specific sleep disorders which the sleep system 100 is configured
to detect. The data may, for example, be stored in memory
associated with the sleep system 100.
[0240] At 904, the sleep system detects a sleep disorder.
Techniques for detecting sleep disorders are described below and
vary based on the specific disorder being detected.
[0241] In at least some embodiments, at 906, the one or more
processors may be configured to trigger an alert via an output
interface associated with the sleep system when one or more of the
sleep disorders are detected. For example, the alert may be
provided on a display 390 (FIG. 3) of the sleep system 100 and/or
on a display 1290 (FIG. 12) of a mobile device 1200 (FIG. 12)
associated with the sleep system.
[0242] In at least some embodiments, the alert may only be
triggered if the sleep disorder appears to exist for a
predetermined number of nights. For example, in some embodiments,
the alert will be triggered only if the processor detects the sleep
disorder for an occupant for a consecutive number of nights.
[0243] Various sleep disorders which may be detected by the sleep
system 100 will now be described. The sleep system 100 may be
configured to detect any one or more of the sleep disorders
described below and any combinations thereof.
Insomnia Detection
[0244] In at least some embodiments, the one or more processor(s)
may be configured to detect insomnia. Insomnia is a sleep disorder
in which the occupant has an inability to fall asleep or to stay
asleep as long as desired.
[0245] In at least some embodiments, insomnia may be detected at
step 904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100 based on sleep onset latency
for an occupant. A method 500 for determining sleep onset latency
is described in greater detail above with reference to FIG. 5 and,
in particular, with reference to step 508.
[0246] In at least some embodiments, sleep onset latency may be
compared (at 904 of the method 900 of FIG. 9) to a predetermined
threshold to determine whether the occupant has insomnia. The
predetermined threshold may effectively indicate a sleep onset
latency which is considered too long. In some embodiments, if the
threshold is exceeded (i.e. if it takes too long for the occupant
to fall asleep), then the processor may determine that occupant may
have insomnia. In at least some embodiments, the processor may
quantify the likelihood that the occupant has insomnia based on the
sleep onset latency.
[0247] Other indicators of insomnia may be used instead of or in
addition to the sleep onset latency in order to detect insomnia.
For example, in some embodiments, the sleep efficiency score (which
will be described in greater detail below) may be considered. In
some embodiments, the number of awakenings may be considered. That
is, the number of times an occupant wakes up over a period of time
(such as a night) may be used to determine whether the occupant has
insomnia. The number of awakenings may be tracked using the
techniques described above with reference to FIG. 5. For example,
during step 504 of the method 500 of FIG. 5, if the processor
detects that the occupant has woken up, a wakeup counter may be
incremented. This wakeup counter may then be used to determine
whether the occupant has insomnia. The wakeup counter may be reset
upon occurrence of a condition; for example, the wakeup counter may
be reset after the occupant has ceased resting on the mattress for
at least a predetermined period of time. Generally, a higher number
of wakings is interpreted as a higher likelihood of insomnia.
[0248] In at least some embodiments, the techniques for detecting
insomnia described above may be used by one or more of the
processors to generate an insomnia likelihood score which indicates
the likelihood that the occupant has insomnia. In at least some
embodiments, this insomnia likelihood score may be expressed as a
probability. In some embodiments, if the insomnia likelihood score
exceeds a threshold, then the processor may determine that an
occupant has insomnia.
Narcolepsy Detection
[0249] In at least some embodiments, the one or more processor(s)
may be configured to detect narcolepsy. Narcolepsy is a sleep
disorder in which a person has an extreme tendency to fall asleep.
More specifically, narcolepsy is a neurological disorder which is
caused by the brain's inability to regulate sleep-wake cycles
normally.
[0250] In at least some embodiments, narcolepsy may be detected at
step 904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100 based on sleep onset latency
for an occupant. A method 500 for determining sleep onset latency
is described in greater detail above with reference to FIG. 5 and,
in particular, with reference to step 508.
[0251] In at least some embodiments, sleep onset latency may be
compared to one or more predetermined thresholds to determine
whether the occupant has narcolepsy. The predetermined threshold
may effectively indicate a sleep onset latency which is considered
too short. In some embodiments, if the sleep onset latency is less
than the threshold (i.e. if it takes too little time for the
occupant to fall asleep), then the processor may determine that
occupant may have narcolepsy. By way of example, a threshold of 10
minutes may be used in some embodiments. In other embodiments, a
threshold of 5 minutes may be used. In yet further embodiments,
multiple thresholds (e.g. 5 minutes and 10 minutes) may be used and
each of these thresholds may suggest likelihood that the occupant
has narcolepsy. For example, if the sleep onset latency is below
the lower threshold (e.g. 5 minutes), then the processor may
determine that it is more likely that the occupant has narcolepsy
than if the sleep onset latency is between the lower threshold and
the higher threshold (e.g. 10 minutes), which also signifies a
possibility of narcolepsy. Accordingly, in at least some
embodiments, the processor may quantify the likelihood that the
occupant has narcolepsy based on the sleep onset latency. For
example, the likelihood that the occupant has narcolepsy may be
expressed as a probability.
[0252] Other indicators of narcolepsy may be used instead of or in
addition to the sleep onset latency in order to detect narcolepsy.
For example, in some embodiments, the processor may identify the
average time for the occupant to transition from NREM stage 1 sleep
to REM sleep and may use this average time to detect narcolepsy.
For example, if the average time is less than a threshold (e.g. 20
minutes), then the processor may determine that the occupant likely
has narcolepsy. The times at which the occupant entered and exited
sleep stages may be determined in the manner described above with
reference to FIG. 5.
[0253] Similarly, the amount of time an occupant spends in NREM
stage 1 before progressing to the next sleep stage may also be
considered by the processor when detecting narcolepsy. For example,
if the average time spent in a NREM stage 1 cycle is is less than a
predetermined threshold, then the processor may determine that the
occupant likely has narcolepsy (i.e. it may detect narcolepsy).
[0254] In at least some embodiments, the techniques for detecting
narcolepsy described above may be used by one or more of the
processors to generate a narcolepsy likelihood score which
indicates the likelihood that the occupant has narcolepsy. In at
least some embodiments, this narcolepsy likelihood score may be
expressed as a probability. In some embodiments, if the narcolepsy
likelihood score exceeds a threshold, then the processor may
determine that an occupant has narcolepsy.
Sleep Apnea Detection
[0255] In at least some embodiments, one or more of the
processor(s) may be configured to detect sleep apnea at step 904 of
the method 900 of FIG. 9. In some embodiments, the processor may
further be configured to detect a sleep apnea classification type.
Sleep apnea is a sleep disorder in which an occupant experiences
pauses in breathing or instances of infrequent or shallow breathing
during sleep. The pauses may be referred to as apnea and the
abnormally shallow breathing events may be referred to as
hypoapnea.
[0256] Sleep apnea may, in some embodiments, be classified as
either obstructive sleep apnea (OSA) or central sleep apnea (CSA).
That is, a processor may determine whether an occupant of the sleep
system 100 suffers from OSA and/or whether the occupant of the
sleep system 100 suffers from CSA.
[0257] OSA is more common than CSA. Central sleep apnea is a
neurological condition which occurs when a person's brain does not
send the appropriate signals to the muscles which control
breathing. This may be contrasted with OSA which is caused due to
an obstruction of the upper airway.
[0258] In at least some embodiments, sound may be used by a
processor to detect sleep apnea. More particularly, in at least
some embodiments, an electrical signal (which may be referred to as
an audio signal) representing received sound waves may be generated
by a microphone 334 associated with the sleep system 100. Based on
this electrical signal, a processor may determine whether an
occupant has sleep apnea. In at least some embodiments, the
processor may determine whether the electrical signal includes
snoring and/or gasping events. In at least some embodiments, the
processor may perform audio processing on the electrical signal to
distinguish non-apnea snoring (i.e. snoring which is not caused by
sleep apnea, which may be referred to as normal snoring) from
apnea-caused snoring (i.e. from snoring caused by sleep apnea). The
signal from the microphone is, in at least some embodiments,
converted into the frequency domain through the use data processing
techniques such as fast Fourier transforms, wavelet analysis, or
linear predictive coding. Cut off filters and bandpass filters may
be used to narrow the frequency range, such as 70-2000 Hz, where
snoring and breathing typically occur. Numerous techniques can be
used by a processor to identify snoring/breathing sounds that are
characteristic of OSA or CSA. For example, the data can be
characterized with a spectral envelope determined using linear
prediction autoregressive modeling. Formant frequencies can be
determined by finding the local maxima of the spectra envelope. The
formant frequencies of OSA patients typically have greater
variability in both snoring and breathing, so identifying these
frequencies can be used by the processor to determine the presence
of OSA. Other techniques involve looking at the frequency
characteristics of the snoring. Simple snoring has a spectrum
characterized by a fundamental frequency with harmonics, whereas
OSA snoring has a spectrum centered around a fundamental frequency
without harmonics. To distinguish between these two types of
snoring, in some embodiments, the processor may consider the ratio
of the power above 800 Hz to the power below 800 Hz in the
electrical signal generated by the microphone. OSA snoring
typically produces sound with higher power above 800 Hz, so ratios
greater than one may represent OSA in some embodiments.
Identification of intra-snoring pitch jumps can also be indicative
of OSA. Also, OSA snoring typically has peak intensity above 1000
Hz, while simple snoring typically has a peak intensity between
100-300 Hz. Other techniques may utilize hidden Markov models or
higher order statistics for analysis of the sound data to determine
snoring/breathing sounds and those that are distinct for OSA. Thus,
in at least some embodiments, the processor may detect apnea events
in the audio signal. In at least some embodiments, an apnea event
may be characterized by loud snoring or gasping followed by a quiet
period of twenty to thirty seconds in duration and the processor
may analyze the audio signal to detect such characteristics.
[0259] Apnea events typically occur when an occupant is in certain
stages of sleep. More particularly, apnea events typically occur
during NREM stage 3 and REM sleep. In some embodiments, to reduce
audio processing and/or to improve the accuracy of the detection,
the processor may be configured to consider the sleep stage of the
occupant in the sleep apnea analysis. For example, the sleep stage
of the occupant may be determined in the manner described above
with reference to the method 500 of FIG. 5, and in particular with
reference to step 504, and may be used to facilitate the detection
of sleep apnea. In at least some embodiments, sleep stage
information may be used, by the processor, to identify periods of
interest within the audio signal. The periods of interest are
periods in which sleep apnea is more likely to occur. In at least
some embodiments, the periods of interest may be periods where the
occupant has been determined to be in either NREM stage 3 or REM
sleep.
[0260] The identification of periods of interest may be done before
processing the audio signal (which may reduce the amount of
processing) or may be done after the audio is processed (in which
case the audio processing may not be reduced, but the accuracy of
the detection may be improved). Where the periods of interest are
identified before the audio signal is processed, the processor may
analyze portions of the audio signal corresponding to the periods
of interest but may ignore the portions of the audio signal that do
not correspond to the periods of interest. If, instead, the audio
processing is done after the audio signal is analyzed (e.g. after
the processor has already identified possible apnea events), then
in some embodiments the periods of interest may be used to filter
these apnea events. For example, an apnea event identified during
the sleep analysis may be determined by the processor to be a
non-apnea event if it did not occur during a period of
interest.
[0261] Furthermore, in some embodiments, the occupant's respiratory
rate patterns may be used by a processor in the sleep apnea
detection. Apnea episodes have distinct breathing patterns--in OSA,
typically shallow breathing or a pause in breathing for a period
from seconds to minutes will occur, followed by a large gasp,
followed by a return to normal breathing until the next apnea
episode. Shallow breathing or no breathing will manifest as a lower
than normal respiration amplitude possibly paired with inconsistent
or lower respiration rates, while a gasp will produce a larger than
normal respiration amplitude. Identify this unique patterns of
breathing can be used to identify apnea events. No breathing will
result in a zero respiration rate. Force sensors can be used to
confirm that the occupant is still in bed during periods of zero
respiration. In CSA, the occupant's respiratory rate will be zero
for period of time, followed by a return to normal breathing. The
respiratory rate may be determined in the manner described above
with reference to the method 700 of FIG. 7. The respiratory rate
may be used to correlate the audio in the audio signal to the
occupant's breathing pattern. The audio signal and respiration rate
may be used together to distinguish normal breathing/snoring from
OSA and CSA. The number of apnea events is tabulated over a period
of time to determine the severity of the disorder.
[0262] In some embodiments, the respiratory rate may also be used
by a processor to identify which of two occupants is snoring. More
particularly, since the audio signal may contain sleep apnea events
(such as snoring and/or gasping) associated with more than one
occupant, in some embodiments, the audio signal may be co-related
to the respiration rate to select the occupant who is likely
associated with the sleep apnea event.
[0263] In some embodiments, a single apnea event may not, itself,
cause the processor to determine that the occupant has sleep apnea.
For example, in some embodiments, the processor will count the
number of sleep apnea events and will only determine that sleep
apnea has been detected if at least a predetermined number of sleep
apnea events are detected. In some embodiments, the processor will
detect sleep apnea when at least a predetermined number of sleep
apnea events are detected over a predetermined period of time. For
example, in one embodiment, sleep apnea may be detected if five or
more sleep apnea events are detected in an hour. This is known as
the apnea-hypopnea index (AHI) and is a measure of how often an
individual suffering from OSA stops breathing over a certain amount
of sleep time (usually per one hour of sleep time). Measurements of
AHI under 5 are normal, 5-15 is mild, 15-30 is moderate, and above
30 is severe.
[0264] In some embodiments, other information may also be used by
the processor to detect sleep apnea. For example, in some
embodiments, the processor may determine a sleep apnea risk level
associated with an occupant and may use the sleep apnea risk level
when detecting sleep apnea. The sleep apnea risk level may, for
example, be determined by the processor based on one more occupant
characteristics defined in a user profile for the occupant. The
user profile may be input to the sleep system 100 and/or an
associated device (such as a mobile device) using an input
interface such as a keyboard. After this information is input, it
may be stored in memory 372 associated with the sleep system or
device. The occupant characteristics used in the sleep apnea sleep
apnea determination may, for example, include the age, weight,
physical fitness level, height and/or sex of the occupant. In some
embodiments, the occupant characteristics include an indication of
whether the occupant suffers from dry mouth and/or morning
headaches, since these are both factors that may be related to
sleep apnea.
[0265] Thus, the occupant characteristics may be used to determine
a sleep apnea risk level of the occupant and the sleep apnea risk
level may be used when determining whether the occupant has sleep
apnea. The greater the sleep apnea risk level, the more likely the
occupant will be determined to have sleep apnea.
[0266] In at least some embodiments, the techniques for detecting
sleep apnea described above may be used by one or more of the
processors to generate a sleep apnea likelihood score which
indicates the likelihood that the occupant has sleep apnea. In at
least some embodiments, this sleep apnea likelihood score may be
expressed as a probability. In some embodiments, if the sleep apnea
likelihood score exceeds a threshold, then the processor may
determine that an occupant has sleep apnea.
Bruxism Detection
[0267] In at least some embodiments, the one or more processor(s)
may be configured to detect sleep bruxism. Bruxism is a disorder in
which a person excessively grinds their teeth and/or excessively
clenches their jaw. Sleep bruxism is a form of bruxism that occurs
during sleep.
[0268] In at least some embodiments, sleep bruxism may be detected
at step 904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100. In at least some embodiments,
sound may be used by a processor to detect bruxism. More
particularly, in at least some embodiments, an electrical signal
(which may be referred to as an audio signal) representing received
sound waves may be generated by a microphone 334 associated with
the sleep system 100. Based on this electrical signal, a processor
may determine whether an occupant has bruxism. In at least some
embodiments, the processor may determine whether the electrical
signal includes teeth grinding events. The identification of
bruxism by the processor may involve a pattern-based analysis. More
particularly, the processor may compare the audio represented in
the audio signal to a typical pattern resulting from bruxism.
[0269] Based on the audio-analysis, the processor may generate a
bruxism likelihood score which indicates the likelihood that the
occupant has bruxism. In at least some embodiments, this score may
be expressed as a probability. In some embodiments, if the bruxism
likelihood score exceeds a threshold, then the processor may
determine that an occupant has bruxism.
[0270] In some embodiments, other information may also be used by
the processor to detect sleep bruxism. For example, in some
embodiments, the processor may determine a bruxism risk level
associated with an occupant and may use the bruxism risk level when
detecting sleep bruxism. The bruxism risk level may, for example,
be determined by the processor based on one more occupant
characteristics defined in a user profile for the occupant. The
user profile may be input to the sleep system 100 and/or an
associated device (such as a mobile device) using an input
interface such as a keyboard. After this information is input, it
may be stored in memory 372 associated with the sleep system or
device. By way of example, in some embodiments, the user profile
may indicate whether an occupant complains of jaw pain. In some
embodiments, when the occupant complains of jaw pain, the bruxism
risk level is greater than if the occupant did not complain of jaw
pain. When the occupant complains of jaw pain, the bruxism
likelihood score may be increased by the processor; for example,
the bruxism likelihood score may be increased by 30% in some
embodiments. In some embodiments, a threshold used to detect
bruxism may be adjusted by the processor based on the bruxism risk
level. For example, the threshold may be reduced when the occupant
complains of jaw pain so that bruxism is more easily detected for
such an occupant.
Delayed Sleep Phase Syndrome Detection
[0271] In at least some embodiments, the one or more processor(s)
may be configured to detect delayed sleep phase syndrome (DSPS).
DSPS, which may also be referred to as delayed sleep phase disorder
(DSPD) or delayed sleep-phase type (DSPT) is a sleep disorder which
affects the timing of a person's sleep. More particularly, people
with DSPS often require a relatively large period of time to fall
asleep and they often have difficulty waking up in the morning.
[0272] In at least some embodiments, DSPS may be detected at step
904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100 based on sleep onset latency
for an occupant. A method 500 for determining sleep onset latency
is described in greater detail above with reference to FIG. 5 and,
in particular, with reference to step 508.
[0273] In at least some embodiments, sleep onset latency may be
compared (at 904 of the method 900 of FIG. 9) to a predetermined
threshold to determine whether the occupant has DSPS. The
predetermined threshold may effectively indicate a sleep onset
latency which is considered too long. In some embodiments, if the
threshold is exceeded (i.e. if it takes too long for the occupant
to fall asleep), then the processor may determine that occupant may
have DSPS. In at least some embodiments, the processor may quantify
the likelihood that the occupant has DSPS based on the sleep onset
latency. For example, the likelihood that the occupant has DSPS may
be expressed as a probability.
[0274] In some embodiments, the processor may be configured to
determine that the occupant has DSPS if the occupant experiences a
sleep onset latency which is too long for at least a predetermined
number of days and/or nights. In some embodiments, the processor
may be configured to determine that the occupant has DSPS if the
occupant experiences a sleep onset latency which is too long for at
least a predetermined number of consecutive sleeps. That is, when
the processor detects that the sleep onset latency exceeds a
threshold, it may initiate a counter which counts the number of
consecutive sleeps having excessive sleep onset latency. This
counter may be incremented for each subsequent sleep having
excessive sleep onset latency and may, in some embodiments, be
reset after a sleep without excessive sleep onset latency. When the
counter reaches a threshold, the processor may determine that the
occupant has DSPS.
[0275] The processor may also, in at least some embodiments,
consider the time when an occupant went to bed and/or the time when
the occupant woke up when detecting DSPS. DSPS sufferers tend to go
to bed late and wake up late.
[0276] In at least some embodiments, the ASPS detection may be
performed based on the time of day when an occupant went to bed,
fell asleep, woke up and/or got out of bed. As noted in the
discussion of FIG. 3 above, in some embodiments, the sleep system
100 may include timing circuitry or timing components which are
configured to track the time of day and/or the date. That is, in at
least some embodiments, the sleep system may include a clock. The
clock may be associated with one or more of the processors and may,
in at least some embodiments, be provided on one or more of the
processors. The processor may use timing information obtained from
the clock to detect DSPS. More particularly, the timing information
may be used to track when an occupant went to bed, fell asleep,
woke up and/or got out of bed. Techniques of identifying when an
occupant went to bed, fell asleep and woke up were described above
with reference to the method 500 of FIG. 5. The sleep system may
identify when an occupant got up from bed using a technique that
operates in reverse to the technique for identifying when the
occupant went to bed. For example, when a reading on the force
sensors changes from a state where at least one of the force
sensors in a sensor set 150, 152 is reading a relatively large
amount of force to a state when none of the force sensors in that
same sensor set 150, 152 are reading a relatively large amount of
force, then the processor may determine that an occupant has gotten
up from bed.
[0277] In some embodiments, temperature readings from a body
temperature sensor may be used to detect when an occupant has
gotten up from bed. More particularly, the processor may detect a
decline in temperature sensor as the readings adjust from
representing a body temperature to representing a room temperature.
The processor may interpret such declines in temperature readings
obtained from a body temperature sensor 122 as an indication that
an occupant is or may have gotten up from bed.
[0278] In some embodiments, the processor may detect when an
occupant has gone to bed based on data from one or more of the
force sensors 120a-120h in a sensor set 150, 152. Then the
processor may determine the time when the occupant went to bed
using the clock associated with the processor. The processor may
compare the time when the occupant went to bed to a predetermined
threshold and may determine that the occupant went to bed late if
the time when the occupant went to bed is greater than a
predetermined time threshold. Otherwise (i.e. if the time when the
occupant went to bed is less than the time threshold), then the
processor may determine that the occupant did not go to bed late.
The processor may interpret the occupant going to bed late as an
indication that the occupant may have DSPS.
[0279] In some embodiments, to detect DSPS, the processor may also
monitor when the occupant wakes up. This may be done, for example,
by monitoring whether the occupant is asleep or awake in the manner
described above with reference to FIG. 5. The processor may compare
the time when the occupant woke up to another predetermined time
threshold to determine whether the occupant has woken up late. When
the time when the occupant woke up is greater than this threshold,
then the processor may determine that the occupant has woken up
late. When the time when the occupant woke up is less than this
threshold, then the processor may determine that the occupant has
not woken up late.
[0280] In response to determining that the occupant has went to bed
late and woken up late, the processor may increment a counter. In
some embodiments, the counter tracks the number of days that the
occupant went to bed late and got up late. In some embodiments, the
counter tracks the number of consecutive sleeps that the occupant
went to bed late and got up late (i.e. the number of consecutive
times that the occupant was sleeping in the mattress and went to
bed late and woke up late). This counter may be reset in some
embodiments when a predetermined trigger is detected. This trigger
may, for example, occur when an occupant has gone to bed early or
at a normal time (which may be determined based on a threshold) for
a predetermined number of nights and/or has gotten up early for at
least a predetermined number of nights.
[0281] In at least some embodiments, the processor may determine
that an occupant has DSPS by comparing the counter to one or more
predetermined count thresholds. If the counter exceeds the
threshold, DSPS may be detected.
[0282] In at least some embodiments, sleep onset latency may be
used by the processor together with at least one of the time when a
user went to bed, fell asleep, woke up and/or went to bed, to
detect DSPS.
[0283] In at least some embodiments, the processor may also
consider sleep quality when determining whether an occupant has
DSPS. Sleep quality may, for example, be determined based on the
number of times the occupant wakes up during their sleep session
and/or the amount of time elapsed between when the occupant falls
asleep and when they wake up. A lower number of wakeups results in
a higher sleep quality. In at least some embodiments, since high
sleep onset latency may be an indicator for both DSPS and insomnia,
the processor may be configured to distinguish between these two
conditions based on the sleep quality. Insomnia sufferers tend to
have a low sleep quality, but DSPS sufferers do not tend to have a
low sleep quality. Thus, a measure of the sleep quality may be
compared to one or more predetermined thresholds to determine
whether an occupant has or is likely to have DSPS and/or whether
the occupant has or is likely to have insomnia.
[0284] In at least some embodiments, the techniques for detecting
DSPS described above may be used by one or more of the processors
to generate a DSPS likelihood score which indicates the likelihood
that the occupant has DSPS. In at least some embodiments, this DSPS
likelihood score may be expressed as a probability. In some
embodiments, the probability may be based on the number of
consecutive sleeps during which the occupant experienced excessive
sleep onset latency. For example, when the number of consecutive
sleeps with excessive sleep onset latency reaches a first
predetermined threshold, then the processor may determine that the
likelihood of DSPS is at a first level (e.g. 60%). When the number
of consecutive sleeps with excessive sleep onset latency reaches a
second predetermined threshold, then the processor may determine
that the likelihood of DSPS is at a second level (e.g. 70%). A
greater number of thresholds may be used in other embodiments.
Advanced Sleep Phase Syndrome Detection
[0285] In at least some embodiments, the one or more processor(s)
may be configured to detect advanced sleep phase syndrome (ASPS).
ASPS, which may also be referred to as advanced sleep phase
disorder (ASPD) or advanced sleep phase type (ASPT), is a sleep
disorder in which a person feels very sleepy and goes to bed during
the early evening and wakes up very early in the morning.
[0286] In at least some embodiments, ASPS may be detected at step
904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100. In at least some embodiments,
the ASPS detection may be performed based on the time of day when
an occupant went to bed, fell asleep, woke up and/or got out of
bed.
[0287] As noted in the discussion of FIG. 3 above and in the
discussion of DSPS detection, in some embodiments, the sleep system
100 may include timing circuitry or timing components which allow a
processor to identify a time when an occupant went to bed, fell
asleep and woke up. Techniques for identifying when the occupant
went to bed, fell asleep and woke up were described above with
reference to FIG. 5 and techniques for identifying when an occupant
got up from bed were described with reference to the DSPS detection
described above.
[0288] In some embodiments, the processor may detect when an
occupant has gone to bed based on data from one or more of the
force sensors 120a-120h in a sensor set 150, 152. Then the
processor may determine the time when the occupant went to bed
using the clock associated with the processor. The processor may
compare the time when the occupant went to bed to a predetermined
threshold and may determine that the occupant went to bed early if
the time when the occupant went to bed is less than a predetermined
time threshold. Otherwise (i.e. if the time when the occupant went
to bed is greater than the time threshold), then the processor may
determine that the occupant did not go to bed early. In some
embodiments, the time threshold may be in the range of 6 to 8 pm.
The processor may interpret the occupant going to bed early as an
indication that the occupant may have ASPS.
[0289] In some embodiments, to detect ASPS, the processor may also
monitor when the occupant wakes up. This may be done, for example,
by monitoring whether the occupant is asleep or awake in the manner
described above with reference to FIG. 5. The processor may compare
the time when the occupant woke up to another predetermined time
threshold to determine whether the occupant has woken up early.
When the time when the occupant woke up is less than this
threshold, then the processor may determine that the occupant has
woken up early. When the time when the occupant woke up is greater
than this threshold, then the processor may determine that the
occupant has not woken up early.
[0290] In response to determining that the occupant has went to bed
early and woken up early, the processor may increment a counter. In
some embodiments, the counter tracks the number of days that the
occupant went to bed early and got up early. In some embodiments,
the counter tracks the number of consecutive sleeps that the
occupant went to bed early and got up early (i.e. the number of
consecutive times that the occupant was sleeping in the mattress
and went to bed early and woke up early). This counter may be reset
in some embodiments when a predetermined trigger is detected. This
trigger may, for example, occur when an occupant has gone to bed
late or at a normal time (which may be determined based on a
threshold) for a predetermined number of nights and/or has gotten
up late for at least a predetermined number of nights.
[0291] In at least some embodiments, the processor may determine
that an occupant has ASPS by comparing the counter to one or more
predetermined count thresholds. If the counter exceeds the
threshold, ASPS may be detected.
[0292] In some embodiments, the processor may consider other
information instead of or in addition to the time when an occupant
went to bed or got up from bed. For example, in some embodiments,
the processor may determine an awake latency based on data from one
or more of the force sensors. The awake latency represents the
elapsed time between when an occupant woke up and when they got up
from the mattress. That is, the awake latency indicates the time
when the occupant remained in bed awake after a sleep period. In at
least some embodiments, the awake latency may be compared by the
processor to one or more thresholds to determine whether the awake
latency is too long. In at least some embodiments, the
determination of whether an occupant has ASPS is based on the awake
latency. A long awake latency may, therefore, be interpreted by the
processor as an indication that an occupant has or is likely to
have ASPS.
[0293] In at least some embodiments, the techniques for detecting
ASPS described above may be used by one or more of the processors
to generate an ASPS likelihood score which indicates the likelihood
that the occupant has ASPS. In at least some embodiments, this ASPS
likelihood score may be expressed as a probability. In some
embodiments, the probability may be based on the number of
consecutive sleeps during which the occupant has gone to bed early
and/or woken up early. For example, when the number of consecutive
sleeps during which the occupant has gone to bed early and/or woken
up early reaches a first predetermined threshold, then the
processor may determine that the likelihood of ASPS is at a first
level (e.g. 60%). When the number of consecutive sleeps during
which the occupant has gone to bed early and/or woken up early
reaches a second predetermined threshold, then the processor may
determine that the likelihood of ASPS is at a second level (e.g.
70%). A greater number of thresholds may be used in other
embodiments.
Periodic Limb Movement Disorder
[0294] In at least some embodiments, the one or more processor(s)
may be configured to detect periodic limb movement disorder (PLMD).
PLMD is a sleep disorder in which a person moves limbs
involuntarily during sleep.
[0295] In at least some embodiments, PLMD may be detected at step
904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100. In at least some embodiments,
the PLMD detection may be performed by monitoring movements in a
leg region of the mattress 101. The leg region of the mattress is a
region which is associated with an occupant's legs. Accordingly, in
at least some embodiments, lower body force sensors 120g, 120h may
be used to detect PLMD. That is, the processor may monitor for
movement in the leg region of the mattress by monitoring the lower
body force sensors 120g, 120h. The processor may quantify leg
movement based on readings obtained from the lower body force
sensors 120g, 120h. More particularly, the processor may determine
a measure of leg movement based on data from one or more force
sensors that are located in the leg region (i.e. from the lower
body force sensors 120g, 120h) and may detect PLMD based on the
measure of leg movement.
[0296] In some embodiments, the measure of leg movement may be a
measure of the average number of leg movements over a predetermined
period of time (e.g. movements per hour). If the average number of
leg movements exceeds a predetermined threshold, then the processor
may determine that the occupant has or is likely to have PLMD.
Movements may be detected in the manner described above with
reference to 404 of FIG. 4.
[0297] PLMD movements often occur in NREM stage 1 sleep.
Accordingly, in at least some embodiments, the processor may only
consider movements of the legs that are observed during NREM stage
1 sleep when monitoring for PLMD. That is, movements which occur
during a waking period, during REM sleep, during NREM stage 2 sleep
or during NREM stage 3 sleep may be disregarded when detecting
PLMD. Methods of identifying sleep stage are described in detail
above with reference to FIG. 5.
[0298] In at least some embodiments, the techniques for detecting
PLMD described above may be used by one or more of the processors
to generate a PLMD likelihood score which indicates the likelihood
that the occupant has PLMD. In at least some embodiments, this PLMD
likelihood score may be expressed as a probability. In some
embodiments, the probability may be based on the average number of
leg movements during a predetermined period of time. For example,
when the average number of leg movements reaches a first
predetermined threshold, then the processor may determine that the
likelihood of PLMD is at a first level (e.g. 60%). When the average
number of leg movements reaches a second predetermined threshold,
then the processor may determine that the likelihood of PLMD is at
a second level (e.g. 70%). A greater number of thresholds may be
used in other embodiments.
Sleep Walking Detection
[0299] In at least some embodiments, the one or more processor(s)
may be configured to detect sleep walking. Sleep walking is a sleep
disorder where a sleeping person performs activities that are
usually performed during a full state of consciousness.
[0300] In at least some embodiments, sleep walking may be detected
at step 904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100. In at least some embodiments,
the sleep walking detection may be performed based on the time of
day when an occupant got out of bed.
[0301] As noted in the discussion of FIG. 3 above, in some
embodiments, the sleep system 100 may include timing circuitry or
timing components which are configured to track the time of day
and/or the date. That is, in at least some embodiments, the sleep
system may include a clock. The clock may be associated with one or
more of the processors and may, in at least some embodiments, be
provided on one or more of the processors. The processor may use
timing information obtained from the clock to track when an
occupant got out of bed. Techniques of identifying when an occupant
got out of bed are described in greater detail above in the
discussion of detection of ASPS and these same techniques may be
used for detecting when an occupant has gotten out of bed in order
to detect sleep walking.
[0302] In some embodiments, the processor may log information (i.e.
may store data in memory) indicating the times when an occupant got
out of bed. In at least some embodiments, these times may later be
presented to a user via a display and the user may indicate whether
they recall getting out of bed at the indicated times. If the user
does not recall at least a predetermined number of instances where
they got out of bed, then the processor may determine that the user
is likely to suffer from sleep walking.
[0303] In at least some embodiments, the techniques for detecting
sleep walking described above may be used by one or more of the
processors to generate a sleep walking likelihood score which
indicates the likelihood that the occupant is a sleep walker. In at
least some embodiments, this sleep walking likelihood score may be
expressed as a probability.
Sleep Talking Detection
[0304] In at least some embodiments, the one or more processor(s)
may be configured to detect sleep talking. Sleep talking occurs
when a person talks aloud while asleep.
[0305] In at least some embodiments, sleep talking may be detected
at step 904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100. In at least some embodiments,
sound may be used by a processor to detect sleep talking. More
particularly, in at least some embodiments, an electrical signal
(which may be referred to as an audio signal) representing received
sound waves may be generated by a microphone 334 associated with
the sleep system 100. Based on this electrical signal, a processor
may determine whether an occupant talks in their sleep. In at least
some embodiments, a frequency-based analysis may be performed on
the audio signal to determine whether the audio signal includes
sound associated with a human voice. Typically, humans speak at a
frequency of 300 to 3500 Hz. In at least some embodiments, in
detecting sleep talking, the processor may determine whether the
audio signal includes sound at a frequency associated with a human
voice.
[0306] Based on the audio-analysis, the processor may generate a
sleep talking likelihood score which indicates the likelihood that
the occupant talks in their sleep. In at least some embodiments,
this score may be expressed as a probability. In some embodiments,
if the sleep talking likelihood score exceeds a threshold, then the
processor may determine that an occupant talks in their sleep.
[0307] In some embodiments, audio may be recorded in memory (for
example, in a buffer) and audio containing sleep talking events may
be recorded in a more permanent memory to allow a user to later
listen to their sleep talking session. Playback may be provided
either through a speaker associated with the sleep system 100 or on
a speaker on a mobile device 1200 (FIG. 12) or on a client device
accessing a web server 1300 (FIG. 13).
Bedwetting Detection
[0308] In at least some embodiments, the one or more processors may
be configured to detect bedwetting, which may also be referred to
as nocturnal enuresis or nighttime urinary incontinence. This is a
condition in which a person urinates in bed.
[0309] In at least some embodiments, bedwetting may be detected at
step 904 of the method 900 of FIG. 9 by one or more processors
associated with the sleep system 100. In at least some embodiments,
sound may be used by a processor to detect bed wetting. More
particularly, in at least some embodiments, an electrical signal
representing a humidity level may be generated by a body humidity
sensor 124. In some such embodiments, the processor may compare the
humidity level obtained from the body humidity sensor 124 to one or
more predetermined thresholds to detect bedwetting. In some
embodiments, when the humidity level exceeds one of the thresholds,
then the processor may determine that bedwetting has occurred.
[0310] In at least some embodiments, the processor may also
consider whether the occupant is in the bed and/or whether the
occupant is asleep when determining bedwetting. Methods for
detecting presence of an occupant (i.e. whether the occupant is in
the bed) and whether the occupant is asleep are described in
greater detail with reference to FIG. 5. Excessive humidity
occurring when an occupant is not in bed and/or is not asleep may
be caused by another source, apart from bedwetting. Accordingly, in
at least some embodiments, such humidity readings are ignored by
the processor when detecting bedwetting.
[0311] The processor may, in some embodiments, generate a
bedwetting likelihood score which indicates the likelihood that the
occupant wets the bed. In at least some embodiments, this score may
be expressed as a probability. In some embodiments, if the
bedwetting likelihood score exceeds a threshold, then the processor
may determine that an occupant is a bed-wetter.
Sleep Score Determination
[0312] As noted above, in at least some embodiments, one or more
processors associated with the sleep system 100 may be configured
to determine sleep state information associated with one or more
occupants of the sleep system 100. In at least some embodiments,
this sleep state information may include one or more sleep scores.
The sleep score may, for example, quantify the quality, efficiency
and/or consistency of an occupant's sleep. Methods of determining
sleep scores, such as a sleep efficiency score and/or a sleep
consistency score, will now be discussed.
Sleep Efficiency Score
[0313] In at least some embodiments, one or more processors
associated with the sleep system 100 may be configured to determine
a sleep efficiency score. The sleep efficiency score provides a
quantitative measure of quality and/or efficiency of sleep during a
sleeping period. In some embodiments, this sleeping period may be a
single night. That is, the sleep efficiency score may provide a
quantitative measure of sleep quality during a single night of
sleep. Thus, the metrics and measures described below may, in at
least some embodiments, be determined based on data obtained during
a single night's sleep.
[0314] The sleep efficiency score may be determined, by the
processor, based on one or more of: the sleep onset latency, a
subjective sleep quality metric, a sleep duration, a sleep
efficiency metric, a number of sleep disturbances, an amount of
time in a REM stage, and/or an amount of time in a deep sleep. In
some embodiments, the sleep efficiency score may be determined as a
weighted average of two or more of these metrics and measures.
[0315] The sleep onset latency is a measure of the difference
between the time when an occupant attempted to fall asleep and the
time when that occupant fell asleep. Methods of determining sleep
onset latency are described with reference to 508 of FIG. 5.
[0316] Subjective sleep quality may, in at least some embodiments,
be input by a user via an input interface associated with the sleep
system and/or a mobile device associated with the sleep system. For
example, a user may be presented with a prompt (which may be
displayed on a display) to rate their sleep. Based on the input
received from the user, the subjective sleep quality may be
quantified.
[0317] Sleep duration may be determined by the processor and is a
measure of the total amount of time that an occupant spent
sleeping. That is, sleep duration is the amount of time that
elapsed between the time when the occupant fell asleep and the time
when the occupant woke up. Techniques for identifying when the
occupant fell asleep and woke up are described above.
[0318] The sleep efficiency metric is also determined by the
processor and may be based on the total amount of time that the
occupant was in bed and the amount of time that the occupant spent
sleeping. For example, the sleep efficiency metric may be the
percentage of time in the bed that was spent sleeping. By way of
example, in some embodiments, the processor may calculate the sleep
efficiency metric as the dividend of the time spent sleeping
divided by the total time in the mattress. Techniques for
identifying when the occupant got into bed, fell asleep, woke up
and got out of bed are described in greater detail above and the
times associated with each of these events may be used to determine
the sleep efficiency metric.
[0319] The number of sleep disturbances is a measure of the number
of times an occupant wakes up during a sleep session (e.g. during
the course of a night). In at least some embodiments, a sleep
disturbance may be caused by an environmental factor, such as noise
in the room where the occupant is sleeping, or it may not be caused
by such environmental factors and may be part of that occupant's
sleep routine (e.g. it may be caused by a sleep disorder such as
sleep apnea). The number of sleep disturbances may be tracked by
incrementing a counter each time a sleep disturbance is detected.
The counter is occupant-specific. That is, sleep disturbances may
be separately tracked for each occupant.
[0320] The amount of time in REM or a metric determined based on
the amount of time in REM may also be used by the processor to
determine the sleep efficiency score. The processor may determine
the amount of time in REM by identifying periods in which an
occupant is in the REM sleep stage and periods in which the user is
not in the REM sleep stage (i.e. is either awake or in one of the
non-REM stages) using the techniques described above with reference
to FIG. 5. In at least some embodiments, the processor may
determine a metric which is based on the total amount of time spent
in the REM sleep stage during a single sleeping period or session
(e.g. during a single night) as compared with the total amount of
time spent in other sleep stages and/or the total amount of time
spent in the bed. By way of example, in some embodiments, the
metric may be determined as the dividend of the total time spent in
REM during a sleep period divided by the total time spent in bed
during the sleep period.
[0321] The amount of time in a deep sleep or a metric determined
based on the amount of time in a deep sleep may be used by the
processor to determine the sleep efficiency score. Certain sleep
stages may be considered "deep sleep" stages. In at least some
embodiments, only the NREM stage 3 sleep stage is considered to be
a "deep sleep" stage. The processor may determine the amount of
time in a deep sleep by identifying such sleep stages in the manner
described above with reference to FIG. 5. In at least some
embodiments, the processor may determine a metric which is based on
the total amount of time spent during deep sleep stages during a
single sleeping period or session (e.g. during a single night) as
compared with the total amount of time spent in other sleep stages
and/or the total amount of time spent in the bed. By way of
example, in some embodiments, the metric may be determined as the
dividend of the total time spent in deep sleep stages during a
sleep period divided by the total time spent in bed during the
sleep period.
[0322] Accordingly, a sleep efficiency score may be determined in
the manner described above. In at least some embodiments, once
determined, the sleep efficiency score may be stored in memory
associated with the sleep system 100. In some embodiments, after
the sleep efficiency score is determined, it may be output through
an output interface associated with the sleep system 100. For
example, the sleep efficiency score may be displayed on a display
associated with the sleep system and/or an associated mobile device
1200.
Sleep Consistency Score
[0323] In at least some embodiments, one or more processors
associated with the sleep system 100 may be configured to determine
a sleep consistency score. The sleep consistency score provides a
quantitative measure of quality and/or efficiency of sleep over an
extended period of time. In some embodiments, this period of time
may be a plurality of consecutive sleep sessions, such as a
plurality of consecutive nights. For example, in some embodiments,
this period of time may be the last two weeks.
[0324] The sleep consistency score may be determined, by the
processor, based on one or more of: the sleep onset latency, a
subjective sleep quality metric, a sleep duration, a sleep
efficiency metric, a number of sleep disturbances, an amount of
time in a REM stage, and/or an amount of time in a deep sleep. In
some embodiments, the sleep efficiency score may be determined as a
weighted average of two or more of these metrics and measures.
[0325] The sleep consistency score may also, in at least some
embodiments, consider the variation in the time when an occupant
goes to bed and/or wakes up. That is, the time when the occupant
goes to bed and/or wakes up may be tracked over several sleep
sessions (e.g. several nights) and the processor may determine a
measure of the variability for one or both of these times. This
measure of variability may be used by the processor when generating
the sleep consistency score.
[0326] These metrics are described in greater detail above with
reference when the method for determining a sleep efficiency score
was described. The sleep consistency score differs from the sleep
efficiency score in that it considers multiple sleep sessions.
[0327] In at least some embodiments, the sleep consistency score
may be determined by comparing data from a most recent sleep
session (e.g. from the previous night) to data from a plurality of
prior sleep sessions. For example, a moving average over a
predetermined number of sleep sessions may be used to determine the
variability or standard deviation of one or more of the metrics
noted above over the period.
[0328] Accordingly, a sleep consistency score may be determined in
the manner described above. In at least some embodiments, once
determined, the sleep consistency score may be stored in memory
associated with the sleep system 100. In some embodiments, after
the sleep consistency score is determined, it may be output through
an output interface associated with the sleep system 100. For
example, the sleep consistency score may be displayed on a display
associated with the sleep system and/or an associated mobile
device.
Mattress Health Information Determination
[0329] In at least some embodiments, one or more of the processors
associated with the sleep system 100 may be configured to determine
mattress health information. Mattress health information is
information about the health of the mattress 101. The mattress
health information may, for example, quantify the usage of the
mattress over its lifetime (i.e. since manufacture of the
mattress), quantify the usage of the mattress since a maintenance
event (such as the usage since a last flip or rotation of the
mattress, the usage since the last vacuuming of the mattress, the
usage since the last change of bedding, the usage since the last
deodorizing and/or disinfecting of the mattress), and/or may be
based on the humidity level associated with the mattress.
[0330] In at least some embodiments, an alert may be generated
based on such mattress health information. The alert may, for
example, indicate to a user that maintenance is required.
[0331] Referring now to FIG. 10, an example method 1000 for
monitoring mattress health is illustrated in flowchart form. In at
least some embodiments, one or more of the processors that are
included in the sleep system 100 or in a server, system or device
that is coupled to the sleep system may be configured to determine
mattress health information for an occupant based on data obtained
from one or more sensors embedded within the mattress 101. The one
or more processors may include, for example, the main processor
117, the microprocessors 130a, 130b, a processor provided on an
external peripheral of the type described above, a processor 1217
on a mobile device 1200 connected or connectable to the sleep
system 100, a processor on a remote server connectable to the sleep
system 100, and/or another processor associated with the sleep
system 100.
[0332] More particularly, one or more memories associated with the
one or more processors may include processor-executable
instructions which, when executed, configure the processor to
perform the method 1000. For example, in some embodiments, memory
372 associated with the main processor 117 may include such
processor-executable instructions to configure the main processor
117 to perform the method 1000.
[0333] The method 1000 described below may be used to determine
mattress health information. At 1002, the processor obtains data
from one or more sensors that are embedded within the mattress.
These sensors may include, for example, one or more force sensors
120a-120h and/or a humidity sensor 124.
[0334] At 1004, the processor determines mattress health
information based on the data obtained from the one or more
sensors.
[0335] In some embodiments, step 1004 may include a plurality of
sub-steps which allow the processor to quantify mattress usage.
That is, the processor may determine one or more numerical
representations of the amount of usage of the mattress 101. As
described above with reference to FIG. 1, in at least some
embodiments, one or more force sensors may be embedded into the
mattress and may be positioned within the mattress to sense
presence of an occupant of the mattress. That is, the force sensors
are positioned so that at least one of the force sensors is engaged
when an occupant is lying in the mattress 101 in a typical sleeping
position. In at least some such embodiments, the mattress usage may
be quantified based on data from one or more of the force
sensors.
[0336] More particularly, the force sensors may be used to detect
whether the mattress is in use (at 1006). When the processor
determines, based on data from the force sensor(s) that the
mattress is in use, it may track the amount of time which the
mattress is in use. That is, the processor may detect that the
mattress is in use when an occupant goes to bed (i.e. when they
enter the bed). When this happens, the processor may record the
time when the occupant went to bed in memory associated with the
processor.
[0337] The time when an occupant went to bed is the time when the
occupant laid on the mattress after having previously not been on
the mattress. As noted above, this time may be identified by the
processor based on data from the force sensors 120a-120h. That is,
when an occupant goes to bed (i.e. lays on the mattress 101), the
processor identifies a large increase in the force measured on at
least some of the force sensors. Thus, the processor may determine
that an occupant enters the bed when the force measured at a
predetermined number (which may be one in some embodiments) of the
force sensors 120a-120h exceeds a predetermined threshold.
[0338] In some embodiments, a further check may be performed to
confirm that the change in force was due to an occupant entering
the mattress and not, for example, due to an object being placed on
the mattress. For example, a temperature may be obtained from a
temperature sensor 122 and compared to a threshold to determine
that an occupant has entered the mattress. Furthermore, in at least
some embodiments, the processor may require that at least a
predetermined number of force sensors are engaged (e.g. are
registering forces which exceed one or more thresholds) and/or may
require that specific force sensors are engaged before determining
that an occupant has entered the mattress. For example, if an upper
body force sensor registers a force which exceeds a predetermined
threshold, but a middle body force sensor does not register a force
which exceeds a predetermined threshold, then the processor may
determine that the occupant has not yet entered the bed; the force
registered at the upper body force sensor may be caused by an
object apart from a human occupant.
[0339] The sleep system 100 (and more particularly, a processor
associated with the sleep system) may then detect that the mattress
101 is no longer in use at 1008. More specifically, the processor
detects that the occupant has gotten up from bed. This may be
detected, by the processor, using a technique that operates in
reverse to the technique for identifying when the occupant went to
bed. For example, when a reading on the force sensors changes from
a state where at least one of the force sensors in a sensor set
150, 152 is reading a relatively large amount of force to a state
when none of the force sensors in that same sensor set 150, 152 are
reading a relatively large amount of force, then the processor may
determine that an occupant has gotten up from bed.
[0340] In some embodiments, temperature readings from a body
temperature sensor may be used to detect when an occupant has
gotten up from bed. More particularly, the processor may detect a
decline in temperature sensor as the readings adjust from
representing a body temperature to representing a room temperature.
The processor may interpret such declines in temperature readings
obtained from a body temperature sensor 122 as an indication that
an occupant is or may have gotten up from bed.
[0341] After detecting that an occupant has left the bed, the
processor may determine, at 1010, the amount of time that the
occupant was in the bed during their last sleep session. That is,
the processor may determine the amount of time elapsed between when
the mattress was detected to be in use and when the mattress was
detected to be no longer in use.
[0342] The processor may then update (at 1012) one or more
numerical representations of usage stored in memory associated with
the processor. For example, in some embodiments, the memory may
store one or more numerical representations of usage which indicate
the usage of the mattress since a last maintenance event. One such
numerical representation may be referred to as
usage-since-maintenance information. The usage-since-maintenance
information indicates usage of the mattress since a last
maintenance event of a predetermined type. In some embodiments, the
usage-since-maintenance information may indicate the usage of the
mattress since it was last flipped and/or rotated. In some
embodiments, the usage-since-maintenance information may indicate
the usage of the mattress since it was last deodorized and/or
disinfected. In some embodiments, the usage-since-maintenance
information indicates the usage of the mattress since it was last
vacuumed. In some embodiments, the usage-since-maintenance
information indicates the usage of the mattress since the bedding
(e.g. sheets) were last changed. The usage-since-maintenance
information may indicate the usage of the mattress since other
maintenance events in other embodiments. Further, it will be
appreciated that the memory may store multiple types of
usage-since-maintenance information and may separately track each
type of such information. For example, the memory may store
usage-since-maintenance information indicating usage since the last
flip or rotation of the mattress and may store separate usage-since
maintenance information indicating usage since the last time the
bedding was changed.
[0343] In at least some embodiments, the processor may determine
new usage-since-maintenance information by adding the amount of
time elapsed between when the mattress was detected to be in use
and when the mattress was detected to be no longer in use to the
usage-since-maintenance information stored in memory. That is, the
usage-since-maintenance stored in memory is updated to include
usage from the most recent sleep session. The memory may then be
updated to store the new usage-since-maintenance information.
[0344] In some embodiments, the numerical representations of usage
stored in memory associated with the processor may include
lifetime-usage information. The lifetime usage information
indicates the total usage of the mattress since manufacture; that
is, usage over the lifetime of the mattress. The lifetime-usage
information is, in at least some embodiments, never reset since the
lifetime-usage information acts as a type of odometer to track
total usage of the mattress over its life. In at least some
embodiments, after determining an amount of time that elapsed
between when the mattress was detected to be in use and when the
mattress was detected to no longer be in use, the processor may
determine new lifetime-usage-information at 1010. The new
lifetime-usage-information is determined by adding the amount of
time elapsed between when the mattress was detected to be in use
and when the mattress was detected to no longer be in use to the
lifetime-usage information stored in memory. Then, at 1012, the
processor may update the memory to store the new lifetime-usage
information.
[0345] At 1014, an alert may be triggered based on the mattress
health information. For example, an alert may be triggered based on
the numerical representations of usage discussed above (i.e. the
usage-since-maintenance information and/or the lifetime-usage
information). More specifically, one or more of the numerical
representations of usage may be compared, by the processor, to one
or more predetermined thresholds (which may be stored in memory)
and an alert triggered at 1014 based on the result. For example,
when a threshold is exceeded, the alert may be generated.
[0346] The alert may, for example, be generated on an output
interface associated with the sleep system 100, such as a display.
Accordingly, in at least some embodiments, the processor is
configured to output an alert via an output interface in response
to determining that mattress maintenance is required.
[0347] In at least some embodiments, at 1014,
usage-since-maintenance information is compared to an associated
predetermined threshold. In at least some embodiments, the alert
may be generated by the processor in response to determining that
the usage-since-maintenance information exceeds the associated
predetermined threshold. The predetermined threshold(s) compared to
the usage-since-maintenance information represent time periods
after which a maintenance event should be performed. Thus, by
comparing the usage-since-maintenance information to its associated
threshold, the processor determines whether mattress maintenance is
required.
[0348] The thresholds that are used will depend on the nature of
the usage-since-maintenance information being evaluated. For
example, a threshold used to evaluate usage-since-maintenance
information which indicates the amount of use since bedding was
last changed may be in the range of forty to seventy hours.
Similarly, a threshold used to evaluate usage-since-maintenance
information which indicates the amount of use since a top cover of
the mattress was washed may be in the range of eighty to one
hundred and thirty hours. A threshold used to evaluate
usage-since-maintenance information which indicates the amount of
use since the mattress was deodorized, refreshed and/or disinfected
may be in the range of forty to seventy hours. A threshold used to
evaluate usage-since-maintenance information which indicates the
amount of use since the mattress was vacuumed may be in the range
of two hundred to two hundred and fifty hours. In some embodiments,
the threshold used to evaluate usage-since-maintenance information
which indicates the amount of use since the mattress was flipped
and/or rotated may be in the range of one week to three months. In
at least some embodiments, the threshold used by the processor to
evaluate usage-since-maintenance information may depend on home
long the mattress has been in use over its lifetime (e.g. it may
depend on the lifetime-usage information). For example, in some
embodiments, certain maintenance events may be required more
frequently when the mattress is new. By way of example, more
frequent flipping or rotation may be required when the mattress is
new (e.g. flipping/rotation may be required weekly when new, but
monthly when older). Similarly, in some embodiments, certain
maintenance events may be required more frequency when the mattress
is old (e.g. deodorizing and/or disinfecting may be more frequent
when the mattress is older). Thus, in at least some embodiments,
the processor may select a threshold to be used for evaluating
usage-since-maintenance information based on the age of the
mattress (e.g. based on the lifetime-usage information).
[0349] The nature of the alert that is generated may also depend on
the type of usage-since-maintenance information which was found to
exceed the associated threshold. For example, in some embodiments,
when the usage-since-maintenance information suggests that it has
been too long since the last flip and/or rotation, the alert may be
a displayed message prompting a user to flip or rotate the
mattress. The alert may, in other situations, prompt the user to:
change the bedding, wash the top cover, deodorize, refresh and/or
disinfect the mattress, and/or vacuum the mattress.
[0350] Where lifetime-usage information is obtained, this
information may also be compared, at 1014, to an associated
predetermined threshold. In some embodiments, in response to
determining that the lifetime-usage information exceeds the
predetermined threshold, an associated alert may be triggered. This
alert may prompt the user to replace the mattress.
[0351] While the lifetime-usage information may not be reset,
usage-since-maintenance information may be reset when the user
completes an associated maintenance activity. For example, if the
user changes the bedding, the usage-since-maintenance information
which indicates the amount of time in which the bedding was in use
may be reset. More particularly, usage-since-maintenance
information may be reset by the processor when one or more
predetermined reset conditions are detected. In some embodiments,
an input interface may be provided on the sleep system 100 or an
associated mobile device to allow a user to input instructions. In
some embodiments, one or more of the predetermined reset conditions
includes an instruction to reset specific usage-since-maintenance
information. This instruction may be received via the input
interface. For example, a user may use the input interface to
inform the processor that the bedding has recently been changed,
which may then cause the processor to reset the
usage-since-maintenance information that tracks the amount of time
that the bedding was in use.
[0352] In some embodiments, other reset conditions may be used. For
example, in some embodiments, a flip of the mattress may be
detected using the force sensors embedded into the mattress. In yet
other embodiments (not shown) the sleep system 100 may include one
or more orientation or acceleration sensors which may be used, by
the processor, for detecting a mattress flip. Such sensors may
include, for example, accelerometers, gyroscopes, magnetometers,
etc.
[0353] "Flipping" the mattress and "rotating" the mattress are used
herein to mean different actions. A mattress flip occurs when the
side which is the upper side changes. That is, the side of the
mattress that supports an occupant changes during a "flip" so that
the side which supported the occupant before the flip no longer
supports the occupant and is, instead, closer to the floor. In
contrast, during a mattress rotation, the upper side does not lose
its status as the upper side. More particularly, the side of the
mattress which supported the occupant before the rotation continues
to support the occupant after the rotation.
[0354] It will be appreciated that at least some of the sensors
described above (e.g. the force sensor, accelerometers, gyroscopes,
magnetometers, etc.) may be used to detect the orientation of the
mattress. For example, the processor may determine which of the
sides is currently the "upper" side based on data from one or more
of these sensors. Furthermore, in some embodiments, the processor
may determine which of the sides is currently a "headboard" side by
analyzing data from one or more of these sensors. For example,
headboard side may be determined based on the distribution of
forces at the force sensors. In some embodiments, an input
interface may be used to allow a user to specify which of the sides
of the mattress is a top side and/or which of the sides is a
headboard side.
[0355] To allow for mattress rotation, in at least some
embodiments, the sensors embedded within the mattress may have
rotational symmetry. An object is said to have rotational symmetry
if it looks the same after a certain amount of rotation. A second
order rotational symmetry means that the object looks the same
after one hundred and eighty degrees of rotation. In a at least
some embodiments, the force sensors that are embedded into the
mattress and that are associated with the top side of the mattress
are arranged to have a second order rotational symmetry to
accommodate rotation of the mattress. It will be appreciated that
the arrangement of force sensors illustrated in FIG. 1 did not have
such rotational symmetry. However, the arrangement of FIG. 1 could
be modified to have such rotational symmetry; for example, by
including a third and fourth sensor set in addition to the first
and second sensor sets 150, 152 illustrated in the example. The
third and fourth sensor sets could be arranged so that they appear
one hundred and eighty degrees out of rotational alignment with the
first and second sensor sets. That is, if the third and fourth
sensor sets were rotated one hundred and eight degrees, they would
line up with the first and second sensor sets.
[0356] To allow for mattress flipping, sensors may be associated
with both a top side of the mattress and a bottom side of the
mattress. For example, force sensors may be located near the top
side and other force sensors may be located near the bottom side.
In some embodiments, the arrangement of the sensors on the top side
is the same as the arrangement of the sensors on the bottom
side.
[0357] As noted in the discussion of FIG. 1, in at least some
embodiments, the mattress may be configured for use by two
occupants. In some such embodiments, the usage information
described above (such as the usage-since-maintenance information
and/or the lifetime-usage information) may be separately tracked
for each occupant. That is, usage may be separately tracked for
each of two sides of the mattress. In some such embodiments, the
memory may store first usage-since-maintenance information
indicating total usage of a first side of the mattress since a last
maintenance event and second usage-since-maintenance information
indicating total usage of a second side of the mattress since the
last maintenance event. The processor may be configured to modify
the first usage-since-maintenance information based on detected
usage of the first side of the mattress and to modify the second
usage-since-maintenance information based on detected usage of the
second side of the mattress. In some embodiments, the processor is
configured to compare both the first usage-since-maintenance
information and the second usage-since-maintenance information to a
predetermined threshold and to generate the alert in response to
determining that any one or both of the first
usage-since-maintenance information and the second
usage-since-maintenance information exceed the predetermined
threshold. That is, if either side of the bed has been used too
much since the maintenance event, then the alert may be triggered.
In other embodiments, the usage-since-maintenance and
lifetime-usage information may not be separately tracked for each
occupant.
[0358] Furthermore, other sensors may be used obtain mattress
health information and to generate associated alerts instead of or
in addition to the force sensors. For example, in some embodiments,
the sleep system 100 includes a humidity sensor 124 which may be
embedded in the mattress or included in a peripheral. In at least
some embodiments, the processor may generate an alert based on data
obtained from the humidity sensor. For example, in some
embodiments, the processor is configured to generate the alert if a
humidity level obtained from the humidity sensor exceeds a
threshold for at least a predetermined period of time. The
threshold and/or the time may be selected to prevent mold
growth.
Sleeping Condition Monitoring and Reporting
[0359] In at least some embodiments, one or more of the processors
associated with the sleep system 100 may be configured to monitor
sleeping conditions. More particularly, in some embodiments, the
one or more processors associated with the sleep system 100 may be
configured to determine sleep environment information. Sleep
environment information is information about the sleeping
conditions for an occupant. The sleep environment information may,
for example, identify and/or evaluate conditions in the room in
which the sleep system 100 is located. The sleep environment
information and/or the conditions that are identified and/or
evaluated based on the sleep environment information may, in some
embodiments, be referred to as sleep hygiene information.
[0360] In at least some embodiments, an alert may be generated
based on such sleep environment information. The alert may, for
example, indicate to a user that the sleep environment should be
improved.
[0361] Referring now to FIG. 11, an example method 1100 for
determining sleep environment information is illustrated in
flowchart form. In at least some embodiments, one or more of the
processors that are included in the sleep system 100 or in a
server, system or device that is coupled to the sleep system may be
configured to determine sleep environment information for an
occupant based on data obtained from one or more sensors embedded
within the mattress 101 and data obtained from one or more sensors
provided in a peripheral. The one or more processors may include,
for example, the main processor 117, the microprocessors 130a,
130b, a processor provided on an external peripheral of the type
described above, a processor on a mobile device connected or
connectable to the sleep system 100, a processor on a remote server
connectable to the sleep system 100, and/or another processor
associated with the sleep system 100.
[0362] More particularly, one or more memories associated with the
one or more processors may include processor-executable
instructions which, when executed, configure the processor to
perform the method 1100. For example, in some embodiments, memory
372 associated with the main processor 117 may include such
processor-executable instructions to configure the main processor
117 to perform the method 1100.
[0363] The method 1100 described below may be used to determine
sleep environment information. At 1102, the processor obtains data
from one or more sensors embedded in the mattress 101 and also from
one or more sensors provided in the peripheral which is external to
the mattress (i.e. from the sleep environment sensing array 306).
The peripheral may, for example, be coupled with a processor
embedded into the mattress 101 via a wired or wireless
connection.
[0364] In at least some embodiments, at 1102 data is obtained from
one or more of: the force sensors 120a-120h embedded in the
mattress, a dust sensor provided in the peripheral, a humidity
sensor provided in the peripheral (humidity readings from this
sensor may indicate a room humidity level), a light sensor provided
in the peripheral (which may be used to provide light level
readings for the room in which the mattress is located), a
microphone provided in the peripheral (which may be used to provide
ambient noise readings for the room in which the mattress is
located), and/or a temperature sensor provided in the peripheral
(which may be used to provide room temperature readings for the
room in which the mattress is located).
[0365] At 1104, the data obtained from the sensors is used to
determine sleeping environment information. More particularly, the
sleeping environment information is obtained based on the data from
one or more of the sensors embedded within the mattress and also
based on data from one or more sensors provided in the
peripheral.
[0366] The data from the force sensors embedded in the mattress may
be used to determine whether an occupant is in bed and/or sleeping.
Methods for determining whether the occupant is in bed and/or
sleeping are described in greater detail above with reference to
FIG. 5 and such methods may be performed by the processor during
the method 1102 of FIG. 11. More particularly, the processor may
detect a sleep session based on data obtained from the sensors
embedded in the mattress. The sleep session may be said to occur
when an occupant is in bed in some embodiments. In other
embodiments, the sleep session may be said to occur when an
occupant is asleep.
[0367] The sleep environment information may then be obtained based
on data from one or more sensors in the peripheral which was
obtained during the sleep session. Data obtained from the
peripheral when a sleep session was not in progress may be
discarded in at least some embodiments. That is, the sleep
environment information may not consider data obtained from one or
more of the sensors in the peripheral when a sleep session was not
in progress.
[0368] As noted above, in at least some embodiments, the peripheral
may include a dust sensor 338. In some such embodiments, the sleep
environment information may be determined based on dust readings
from the dust sensor. In some embodiments, the sensors provided in
the peripheral include a room humidity sensor 330 and the sleep
environment information is determined based on humidity sensor
readings obtained from the humidity sensor. In some embodiments,
the sensors provided in the peripheral include a light sensor 336
and the sleep environment information is determined based on light
readings obtained from the light sensor. In some embodiments, the
sensors provided in the peripheral include a microphone 334 and the
sleep environment information may be determined based on an audio
signal generated by the microphone. In some embodiments, the
sensors provided in the peripheral include a room temperature
sensor 332 configured to detect the room temperature and the sleep
environment information is determined based on a temperature
reading obtained from the temperature sensor.
[0369] In some embodiments, the sleep environment information may
be a score which is determined based on data from at least two
different types of sensors provided in the peripheral. This score
may be referred to as a sleep environment score and it may indicate
the quality of environmental factors (such as sound, humidity,
temperature, dust, etc.) in a room in which the mattress is
located.
[0370] In some embodiments, after determining sleep environment
information, the processor may store such information in
memory.
[0371] At 1106, an output may be generated on an output interface
based on the sleep environment information. The output interface
may, for example, be a display associated with the sleep system or
a mobile device. In at least some embodiments, the output may
indicate the sleep environment information. For example, the output
may indicate a humidity level, dust level, light level, sound
level, and/or temperature level in the room.
[0372] In at least some embodiments, an alert may be generated
based on the sleep environment information. For example, in at
least some embodiments, one or more predetermined thresholds may be
used to evaluate humidity levels, dust levels, light levels, sound
levels, and/or temperature levels in the room. For example, a
humidity level, dust level, light level, sound level and/or
temperature level which is determined based on data from one or
more sensors in the peripheral may be compared by the processor to
one or more associated predetermined threshold. In at least some
embodiments, the processor may generate an alert if a level exceeds
associated threshold (or is less than the threshold, depending on
the nature of the threshold). For example, if the room is not humid
enough (i.e. if the humidity level is less than the associated
threshold), an alert may be generated.
Mobile Device
[0373] As noted above, in at least some embodiments, a mobile
device 1200 may connect to the sleep system using a wireless
communication subsystem 370 provided on the sleep system 100. An
example of one such mobile device 1200 will now be discussed with
reference to FIG. 12. The mobile device 1200 is illustrated in
block diagram form. The mobile device 1200 may, in some
embodiments, be a smartphone. In other embodiments, the mobile
device 1200 may be a tablet computer. The mobile device 1200 may
take other forms in other embodiments.
[0374] The mobile device includes a controller which controls
overall operation of the mobile device. In the example, this
controller is provided by a main processor 1217. The main processor
1217 connects to various device subsystems such as, for example, a
wireless communication subsystem 1270, a display 1290, an input
interface 1282, a power source 1212, a camera 1280 and/or a memory
1272. It will be appreciated that the mobile device 1200 will
include other components that are not specifically illustrated.
[0375] The wireless communication subsystem 1270 is used for
connecting the mobile device to the sleep system 100. Once
connected, the mobile device 1200 may send data to and receive data
from the sleep system 100. More particularly, the wireless
communication subsystem 1270 provides for communications between
the main processor 1271 of the mobile device and the main processor
117 of the sleep system 100. The mobile device 1200 may, for
example, receive mattress health information, sleep state
information and/or sleep environment information from the sleep
system 100. In some embodiments, raw sensor data may be received
from the sleep system 100.
[0376] The display 1290 is an output interface which is used for
outputting information from the mobile device. By way of example,
in some embodiments, display screens may be generated on the
display based on mattress health information, sleep state
information and/or sleep environment information received from the
sleep system 100.
[0377] The input interface 1282 is an input mechanism which allows
a user to input instructions to the mobile device 1200. The input
interface 1282 may take a variety of forms including input buttons
or a touchscreen display.
[0378] The power source 1212 provides power to at least some of the
electrical components of the mobile device 1200. By way of example,
in some embodiments, the power source may be a battery.
[0379] In some embodiments, a camera 1280 may be provided on the
mobile device 1200. The camera includes an image sensor which
generates an electrical signal responsive to received light.
[0380] The processor 1217 is associated with memory 1272. The
memory may store data and processor-executable instructions. The
processor-executable instructions may include a mattress monitoring
application 1290. The mattress monitoring application 1290 may
include instructions which configure the main processor 1217 to
perform one or more of the methods described herein or a portion
thereof. More particularly, the mattress monitoring application
1290 may analyze, process, relay and/or report data obtained via
the wireless communication subsystem 1270 from the sleep system
100.
[0381] In some embodiments, the mattress monitoring application
1290 may transmit a signal to a remote server based on the data
obtained from the sleep system 100. For example, in some
embodiments, the mobile device 1200 may be used as a conduit to
transmit data (such as mattress health information, sleep state
information, sleep environment information and/or raw sensor data)
from the sleep system 100 to the remote server. The mobile device
1200 may transmit data that is received from the sleep system (i.e.
it may receive the data at relay it to the server) or it may
transmit data that is obtained at the mobile device 1200 based on
the data received from the sleep system 100. In some embodiments,
the mattress monitoring application 1290 may generate one or more
display(s) based on the data obtained from the sleep system 100.
For example, the mattress monitoring application may generate a
display screen 1500 for display on the display 1290 which includes
one or more sleep disorder indicators 1502 (FIG. 15) to indicate
whether a user (who was an occupant of the sleep system 100 in the
past and/or who has been registered on the mobile device 1200 as
being with the sleep system 100) has one or more sleep disorders
(see FIG. 15 for an example display). In some embodiments the sleep
disorder indicator may indicate that a user has a sleep disorder,
in some embodiments it may indicate that the user does not have a
sleep disorder, in some embodiments it may indicate that a user is
likely to have a sleep disorder, in some embodiments it may
indicate that a user in unlikely to have a sleep disorder, and in
some embodiment the sleep disorder indicator may quantify the
likelihood of the user having a sleep disorder (i.e. it may display
a likelihood score) The sleep disorders may include any of the
sleep disorders described above including insomnia, narcolepsy,
periodic limb movement disorder, DSPS, ASPS, sleep apnea, bruxism,
sleep walking, sleep talking, and bedwetting. Any one or more of
these sleep disorders may be detected by the mobile device 1200, by
the sleep system 100 and/or by a server 1300 (FIG. 13) in the
manner described above with reference to FIG. 9. In at least some
embodiments, the display screens 1500 may provide access to one or
more tips 1504 for dealing with and/or preventing one or more of
these sleep disorders. In the example of FIG. 15, the tips 1504 are
provided as a selectable interface element which may be activated
by an input interface 1282 of the mobile device 1200 (e.g. a
touchscreen display) to cause the processor of the mobile device
1200 to generate a display screen (not shown) which includes text
describing the tip.
[0382] In at least some embodiments, the display screen 1500 (FIG.
15) may include one or more diagnostic report interface elements
1506 which may be activated by an input interface 182 of the mobile
device 1200 to cause the processor to save, print, share (e.g. by
email, social media such as Twitter.TM., on a social network such
as Facebook.TM., etc.) a report based on information obtained from
the sleep system 100. This report may, for example, specify whether
the occupant has a sleep condition and/or may detail information
derived from or based on data obtained from the sleep system's
sensors. For example, the report may provide: information about
when the occupant went to bed (i.e. entered the bed), fell asleep,
woke up, got out of bed; information about the occupant's heart
rate and/or breathing rate during one or more sleep sessions;
information about any sleep apnea events detected during the night;
information about the amount of movements of the occupant;
information about any wakeups during the night; information about
the various sleep stages such as the amount of time spent in each
sleep stage and/or the times when the occupant entered and/or
exited each sleep stage; and/or information about the time(s) when
a user got out of bed during a sleep session (e.g. when they were
sleepwalking). Other information may be included in other
embodiments.
[0383] It will be appreciated that any of the display screens
described below with reference to FIGS. 16 to 21 may include a
diagnostic report interface element 1506 similar to what is
described with reference to FIG. 15 and that the information
contained in the report may depend on the page from which the
report was generated. For example, the report may, as appropriate,
contain: information about the occupant's sleep position(s); sleep
environment information including information about room
temperature, room humidity, room sound, room light and/or room air
quality; mattress health information such as reminders about
maintenance events, etc.
[0384] In some embodiments, the mattress monitoring application
1290 may generate a display screen 1500 (FIG. 15) which includes
one or more sleep position indicators 1510, 1512. The sleep
position indicator(s) are generated based on sleep position
information. Techniques for determining sleep position information
are described with reference to FIG. 8 and such techniques may be
performed by the mobile device 1200, by the sleep system 100 and/or
by a server 1300 (FIG. 13) prior to generating the display screen
1500. In the example of FIG. 15, a first sleep position indicator
1510 indicates the occupant/user's most common sleep position. A
second sleep position indicator 1512 is a pressure map which
visually indicates the frequency that the user sleeps on various
areas of the mattress and/or the amount of force registered at
various force sensors 120a-120h distributed on the mattress
101.
[0385] Referring to FIG. 16, in some embodiments, the mattress
monitoring application 1290 may generate a display screen 1600
which provides sleep environment information. More particularly,
one or more sleep environment indicators 1602, 1604, 1606, 1608,
1610, 1612 may provide information about the sleep environment. In
the example illustrated, a first sleep environment indicator 1602
provides an indication of a rank or scores one or more
environmental factors. For example, in the example, illustrated,
the first sleep environment indicators 1602 ranks the temperature
in the room where the sleep system 100 is located. Other sleep
environment indicators, which are not numbered in FIG. 16, rank the
humidity, sound, light level and/or air quality in the room.
[0386] In the example, illustrated, each of these environmental
factors (temperature, humidity, sound, light level and air quality)
has an associated detailed sleep environment indicator 1604, 1606,
1608, 1610, 1612 which provides additional information about these
environmental factors. For example, these detailed sleep
environment indicators may graph the environmental factors over an
extended period of time. In at least some embodiments, the mobile
device 1600 (and/or the server 1300, as will be explained in
greater detail below with reference to FIG. 13) generates one or
more of these sleep environment indicators 1604, 1606, 1608, 1610,
1612 based only on data obtained while a sleep session was ongoing.
That is, the sleep environment indicators may ignore data obtained
while the occupant was not in bed and/or data obtained while the
occupant was not asleep. Techniques for determining whether an
occupant is in bed and/or asleep are described above. In some
embodiments, data obtained during the daytime may be ignored and
the sleep environment indicators may only be generated based on
data obtained at night.
[0387] In some embodiments, the mattress monitoring application
1290 may include gamification features. Gamification features are
features which set goals and/or which generate awards for a
user/occupant. The gamification features are sleep-related and the
awarding of awards is based on data obtained from the sleep system
100. Accordingly, in at least some embodiments, to implement the
gamification features, the mattress monitoring application 1290 may
cause the processor of the mobile device to determine whether
predetermined sleep criteria associated with an award has been
satisfied based on data obtained from the sleep system 100. If the
sleep criteria associated with the award has been satisfied, then
the processor may generate the award. In at least some embodiments,
the award 1801 (FIGS. 18 and 21) is generated on a display screen
1800 (FIG. 18), 2100 (FIG. 21).
[0388] As illustrated in FIG. 18 (and in the profile page of FIG.
21), the award 1801 may, for example, be in the form of a digital
badge or trophy which may be displayed on a display screen
displayed on the display of the mobile device.
[0389] The sleep criteria associated with an award may, for
example, be based on any one or more of the following factors: the
time when a user/occupant went to bed, the time when a
user/occupant woke up, one or more scores such as a sleep score, a
sleep environment score, a mattress health score, etc, one or more
environmental factors such as the room temperature, room humidity,
light level, air quality, and/or sound during a sleep session,
whether a user attends to a maintenance event and/or the period of
time elapsed between when a user was alerted regarding a
maintenance event and when they indicated that the maintenance
event was complied with. Other criteria may be used in other
embodiments.
[0390] In at least some embodiments, a display screen 1700, 1800
may display an occupant/user's progress toward a goal, level,
achievement and/or an award. For example, a display screen 1702
includes a plurality of progress indicators 1702, 1704, 1706 which
indicate the user's progress towards one or more goals. In this
example, a first progress indicator 1702 indicates progress towards
achieving an increased sleep efficiency level. This progress
indicator may be generated based on a sleep efficiency score, which
is described in greater detail above. The sleep efficiency score
for a sleep session may be determined by the sleep system 100, the
mobile device 1200 and/or the server 1300 (FIG. 13) and may be
added to a total sleep efficiency score that is stored in memory
and which identifies the total sleep efficiency score for prior
sleep sessions. In this way a new total sleep efficiency score is
obtained, and the first progress indicator 1702 is based on this
new total.
[0391] In the example of FIG. 17, a second progress indicator 1704
indicates the user's progress towards achieving an increased sleep
consistency level. This progress indicator may be generated based
on a sleep consistency score, which is described in greater detail
above. The sleep consistency score for a sleep session may be
determined by the sleep system 100, the mobile device 1200 and/or
the server 1300 (FIG. 13) and may be added to a total sleep
consistency score that is stored in memory and which identifies the
total sleep consistency score for prior sleep sessions. In this way
a new total sleep consistency score is obtained, and the second
progress indicator 1704 is based on this new total.
[0392] In the example of FIG. 17, a third progress indicator 1706
indicates the user's progress towards achieving an increased sleep
environment (aka hygiene) score. This progress indicator may be
generated based on a sleep hygiene score, which is described in
greater detail above. The sleep hygiene score for a sleep session
may be determined by the sleep system 100, the mobile device 1200
and/or the server 1300 (FIG. 13) and may be added to a total sleep
hygiene score that is stored in memory and which identifies the
total sleep hygiene score for prior sleep sessions. In this way a
new total sleep hygiene score is obtained, and the third progress
indicator 1706 is based on this new total.
[0393] Referring now to FIG. 18, a further display screen 1802 is
illustrated which also include progress indicators 1802, 1804,
1806, 1808, 1810, 1812. These additional progress indicators
indicate the amount by which the user's progress towards a goal has
changed during their most recent sleep session. In this example,
first and second progress indicators 1802, 1804 illustrate the
amount by which a user's sleep efficiency increased during the last
sleep session. In the case of the first progress indicator 1802
this is indicated relative to the prior progress towards that goal
(i.e. in sleep sessions prior to the most recent sleep session) and
in the case of the second progress indicator 1804 the most recent
progress is indicated in an absolute sense (i.e. not relative to
the prior progress).
[0394] Similarly, third and fourth progress indicators 1806, 1808
may indicate recent progress towards achieving an increased sleep
consistency level and fifth and sixth progress indicators 1810,
1812 may indicate recent progress towards achieving an increased
sleep environment level.
[0395] Accordingly, mattress monitoring application 1290 may, in at
least some embodiments, generate one or more display screens 1700,
1800 which provide feedback to the user about the gamification
features referred to above.
[0396] In some embodiments, the mattress monitoring application
1290 may generate one or more display screens 1700, 1800 which
include one or more of: a sleep time indicator 1710 (FIG. 17), 1836
(FIG. 18) indicating the amount of time that the user slept (this
may indicate the sleep time for the last sleep session (as
indicated by the sleep time indicator 1836 of FIG. 18) and/or over
an extended period such as a plurality of consecutive sleep
sessions (as indicated by the sleep time indicator 1710 of FIG.
17)), a number of times awakened indicator 1712 (FIG. 17)
indicating the number of times that the user woke up (this may
indicate the number of wakeups for the last sleep session and/or
over an extended period such as a plurality of consecutive sleep
sessions), a sleep efficiency indicator 1714 (FIG. 17), 1830
indicating the sleep efficiency score (this may indicate the sleep
efficiency score for the last sleep session (as indicated by the
sleep efficiency indicator 1830 of FIG. 18) and/or over an extended
period such as a plurality of consecutive sleep sessions (as
indicated by the sleep efficiency indicator 1714 of FIG. 17)), a
sleep onset latency indicator 1716 (FIG. 17) which indicates the
amount of time that it took a user to fall asleep (this may
indicate the sleep onset latency for the last sleep session and/or
over an extended period such as a plurality of consecutive sleep
sessions), a bed time indicator 1718 (FIG. 17) indicating the time
at which an occupant went to bed (this may indicate the bed time
for the last sleep session and/or over an extended period such as a
plurality of consecutive sleep sessions), a wake time indicator
1720 (FIG. 17) indicating the time at which an occupant woke up
(this may indicate the wakeup time for the last sleep session
and/or over an extended period such as a plurality of consecutive
sleep sessions), a sleep consistency indicator 1832 (FIG. 18)
indicating a sleep consistency score (this may indicate the sleep
consistency for the last sleep session and/or over an extended
period such as a plurality of consecutive sleep sessions), a sleep
environment (a.k.a. hygiene) indicator 1834 indicating a sleep
environment score (this may indicate the sleep environment score
for the last sleep session and/or over an extended period such as a
plurality of consecutive sleep sessions), a heart rate indicator
(not shown) which may indicate information about the occupants
heart rate for the last sleep session or over an extended period of
time, a respiratory rate indicator (not shown) indicating
information about the occupant's respiration rate during the last
sleep session and/or over an extended period of time, and/or a
sleep stage indicator 1838 which may indicate the times at which an
occupant entered and/or exited sleep stages. Other types of
information that is described in the detailed description above
which is determined based on information obtained from sensors
associated with the sleep system may also be included on display
screens in other embodiments.
[0397] Techniques for determining the various information
represented by the various indicators referred to above are
described above and these techniques may be performed by the mobile
device 1200, sleep system 100 and/or server 1300 (FIG. 13) using
data obtained from the sleep system.
[0398] In some embodiments, the mattress monitoring application
1290 may generate one or more display screens 1900 based on
mattress health information. Techniques for determining mattress
health information are described above (e.g. with reference to FIG.
10) and these techniques may be performed by the mobile device
1200, sleep system 100 and/or server 1300 (FIG. 13) using data
obtained from the sleep system.
[0399] In at least some embodiments, the display screen 1900 may
include one or more alerts 1902, 1904, 1906 that are triggered
based on the mattress health information. These alerts 1902, 1904,
1906 may be generated in the manner described above with reference
to 104 of FIG. 10 and may, for example, indicate whether a
maintenance event is required. In the example illustrated, the
display includes a visual alert 1902 informing the user that it is
time to change the bedding, a visual alert 1904 informing the user
that it is time to refresh the mattress 1904 (which may indicate
that the mattress should be deodorized and/or disinfected) and a
visual alert 1906 informs the user that it is time to rotate and/or
flip their mattress. In some embodiments, one or more indicator may
project an expected date when a maintenance event is required.
[0400] In some embodiments, a display screen 1900 may display other
information about the mattress health. For example,
usage-since-maintenance information may be displayed and/or
lifetime-usage information may be displayed. By way of example, a
total usage indicator 1920 is provided on the display screen 1900
of FIG. 19 to indicate the lifetime-usage information. The display
screens that are generated may, in some embodiments, include one or
more display screens 2000 (FIG. 20) which provide interface
elements for inputting information associated with a user profile
and/or user feedback regarding a sleep session. As noted above,
user profile information may, in some embodiments, be used for
determining a risk level associated with one or more sleep
disorders (e.g. sleep apnea). In some such embodiments, a display
screen 2000 may be generated by the mattress monitoring application
1290 to allow a user to input information about the user and/or a
sleep session. By way of example, the display screen 2000 of FIG.
20 allows a user to input information relevant to a recent sleep
session. This information may include, for example, an overall
rating of the sleep session (as determined by the user), an
indication of whether the user consumed alcohol, exercised late,
consumed caffeine, consumed food late at night, was on medication,
watched television before bed, used their mobile device immediately
before bed, felt stressed, uses a continuous positive airway
pressure (CPAC) device, etc. In at least some embodiments, the
mattress monitoring application 1290 may cause the processor to
correlate such information with nights where the occupant had poor
sleep quality (as reflected by the sleep quality score) and/or when
the occupant suffered from a sleep disorder. In at least some
embodiments, when the results of the correlation suggest that there
is a connection between one of the inputted factors and the poor
sleep quality or sleep disorder, an alert may be generated (e.g. on
a display of the mobile device 1200). This alert may, in at least
some embodiments, be in the form of a suggestion or tip which
suggests the user eliminate or reduce the factor which may have a
causal link to the poor sleep quality or the sleep disorder.
[0401] In at least some embodiments, the mattress monitoring
application 1290 may generate one or more display screens 2100
(FIG. 21) which display information associated with a user profile,
such as a gender, age, weight, height, name, photograph, etc.
associated with a user/occupant.
[0402] While the description immediately above has described an
embodiment in which the mattress monitoring application 1290
associated with the mobile device 1200 generated the display
screens 1500, 1600, 1700, 1800, 1900, 2000, 2100, in other
embodiments, one or more of these display screens may be generated
by a web server 1300 (FIG. 13) which sends such display screens
(e.g. in the form of Hyper Text Markup Language (HTML) documents or
other web-standard documents) to the mobile device 1200. An
Internet browser application which resides in memory on the mobile
device 1200 may receive such display screens 1500, 1600, 1700,
1800, 1900, 2000, 2100 and update the display 1290 accordingly.
Such embodiments will be described below with reference to FIG. 13.
While a single memory is illustrated, in practice the mobile device
1200 includes a plurality of memory components of various
types.
[0403] In at least some embodiments, a code reader application 1292
may be provided in memory of the mobile device 1200. The code
reader application 1292 includes processor-executable instructions
which configure the main processor 1217 to scan a machine-readable
code, such as a QR code and/or a wireless tag such as an NFC tag or
RFID tag (in which case the mobile device may include a short range
communication subsystem such as an NFC subsystem). For example, the
code reader application 1292 may cause the camera 1280 to obtain an
image of a code 180a, 180b (FIG. 1) and to decode information
contained in that code. In some embodiments, the code reader
application may wirelessly receive the code 180a, 180b from a
nearby wireless tag, such as an NFC tag, and may decode information
contained in the received code.
[0404] As noted in the discussion of FIG. 1 above, in some
embodiments, one or more machine readable codes 180a, 180b may be
provided on the mattress 101. In at least some such embodiments,
the information contained in the code may specify a location at
which the mattress monitoring application 1290 may be downloaded,
unique identifying information for the mattress and/or information
which identifies one of the portions 112, 114 of the mattress.
[0405] Where the code specifies a location at which the mattress
monitoring application 1290 may be downloaded, the code reader
application 1292 may be configured to cause the processor 1217 to
automatically download and/or install the mattress monitoring
application 1290 to the mobile device 1200.
[0406] Where the code specifies unique identifying information for
the mattress, the code reader application 1292 and/or the mattress
monitoring application 1290 may use this information to register
the mattress in a user profile for a user of the mobile device.
This user profile may be stored locally on the mobile device or may
be located on a remote server.
[0407] Where the code identifies a specific side of the mattress,
the code reader application 1292 and/or the mattress monitoring
application 1290 may use this information to register the side of
the mattress in a user profile for a user of the mobile device. As
noted in the discussion of FIG. 1 above, in some embodiments, both
portions 112, 114 of the mattress may include two machine-readable
codes 180a, 180b which may be used to associate a mobile device
with a specific side of the mattress. Each of these
machine-readable codes is associated with a separate portion 112,
114 of the mattress 101. For example, a first code 180a may be
located at a left portion 112 of the mattress and associated with
the left portion 112 and a second code 180b may be located at a
right portion 114 of the mattress and associated with the right
portion 114. A user of a mobile device 1200 (FIG. 12) may use the
camera 1280 to scan the code 180a, 180b. The codes 180a, 180b
uniquely identify the mattress from other mattresses, and each of
the codes uniquely identifies the side of the mattress associated
with that code. For example, the first code 180a may identify the
left side and the second code 180b may identify the right side.
[0408] In such embodiments, the code 180a, 180b may be used by the
mobile device to associate the mobile device 1200 with a specific
side of the mattress. That is, an occupant who sleeps on the left
side may scan the code 180a associated with the left side. In at
least some embodiments, by doing so the mattress monitoring
application 1290 will then be automatically configured to obtain
and/or display information obtained from the sleep system about the
left side of the mattress. For example, sleep state information
and/or raw data generated from a first sensor set 150 located at
the left side may be retrieved by the mobile device which has
scanned the code 180a on the left side, but sleep state information
and/or raw data generated from the second sensor set 152 located at
the right side may not be retrieved by the mobile device which has
scanned the code 180a on the left side. Accordingly, in at least
some embodiments, a mobile device 1200 may only retrieve and/or
display information associated with a side of the bed for which it
has scanned the associated code 180a, 180b.
[0409] The mobile device 1200 may include a number of components
that are not illustrated in FIG. 12. By way of example, the mobile
device 1200 could include a number of sensors. In at least some
embodiments, data obtained from the sensors in the mobile device
1200 may be used in conjunction with data obtained from the sensors
embedded into the sleep system 100. For example, one or more of the
sensors in the sleeping environment sensing array 306 (FIG. 3) may
be provided on the mobile device 1200.
Server
[0410] Referring now to FIG. 13, an example server 1300 is
illustrated in block diagram form. The server 1300 is, in at least
some embodiments, a web server which may be configured to host a
website. The web server is, in at least some embodiments,
configured to generate one or more display screens, such as the
display screen(s) 1500, 1600, 1700, 1800, 1900, 2000, 2100 of FIGS.
15 to 16.
[0411] The server 1300 includes a controller which controls overall
operation of the server 1300. In the example, this controller is
provided by a main processor 1317. The main processor 1317 connects
to various device subsystems such as, for example, a communication
subsystem 1370, an input interface (not shown), a power source (not
shown), and/or a memory 1372. It will be appreciated that the
server 1300 will include other components that are not specifically
illustrated.
[0412] The communication subsystem(s) 1370 are used for connecting
the mobile device to other systems, servers and/or devices, such as
the sleep system 100, the mobile device 1200 and/or another client
device such as a computer. More particularly, in at least some
embodiments, the communication subsystem(s) 370 may allow the
server 1300 to receive data from the sleep system 100. Such data
may include, for example, mattress health information, sleep state
information and/or sleep environment information. In some
embodiments, raw sensor data may be received from the sleep system
100. Such data may, in some embodiments, be sent from the sleep
system 100 to the server 1300 using a mobile device 1200 as a
conduit. In other embodiments, the mobile device 1200 may not be
used as a conduit and the data may be sent directly from the sleep
system 100 to the server 1300.
[0413] As noted above, in at least some embodiments, the server
1300 is a web server which is configured to generate display
screens in the form of web pages which may be provided to other
devices, such as the mobile device 1200 of FIG. 12 or a client
device of another type, such as a computer. The web pages may be
displayed via an Internet browser associated with such devices.
[0414] Accordingly, in at least some embodiments, the server 1300
has a mattress monitoring and/or reporting web application 1390
stored in memory 1372. This application 1390 is, in at least some
embodiments, configured to analyze data received from the sleep
system. More particularly, this application may be configured to
cause the processor 1317 to perform any one or more of the methods
described herein to obtain information based on data obtained from
sensors embedded into the sleep system 100. For example, in some
embodiments, the processor may obtain movement information in the
manner described with reference to FIG. 4. In some embodiments, the
processor may determine a sleep stage and/or whether an occupant is
awake and/or a sleep onset or offset latency in the manner
described with reference to FIG. 5. In some embodiments, the
processor may determine a heart rate in the manner described with
reference to FIG. 6 and in some embodiments a respiration rate is
determined in the manner described with reference to FIG. 7. Sleep
position may, in some embodiments, be determined in the manner
described above with reference to FIG. 8. In some embodiments, a
sleep disorder may be detected by the processor 1317 in the manner
described with reference to FIG. 9 and in some embodiments,
mattress health information is determined in the manner described
above with reference to FIG. 10. In some embodiments, sleep
environment information is determined by the processor 1317 using
techniques described with reference to FIG. 11.
[0415] Accordingly, in at least some embodiments, the server 1300
may determine at least some of the information described above. In
some embodiments, the server 1300 may not, itself, determine at
least some of this information but may instead be provided with
this information by the sleep system 100.
[0416] In at least some embodiments, the server 1300 may generate
one or more web pages based on information obtained from the sleep
system 100. These display screens may be of the type described
above with reference to the mobile device 1200 of FIG. 12. That is,
instead of relying on a mattress monitoring application on the
mobile device 1200 to generate these display screens 1500, 1600,
1700, 1800, 1900, 2000, 2100, these display screens may instead be
generated by the web server (i.e. by the processor 1317 executing
the mattress monitoring and/or reporting web application 1390) and
provided to a client device such as the mobile device 1200 for
display via a web browser or for display via a mobile application.
Thus, any one or more of the display screens of FIGS. 15 to 21 may
be generated by the server 1300.
Generating Display Screen(s)
[0417] Referring now to FIG. 14, an example method 1400 of
generating a display screen 1500, 1600, 1700, 1800, 1900, 2000,
2100 is illustrated in flowchart form. The method 1400 may be
performed by a processor associated with the mobile device 1200
(FIG. 12) or server 1300 (FIG. 13). More particularly,
computer-executable instructions such as a mattress
monitoring/reporting web application 1390 (FIG. 13) and/or a
mattress monitoring application 1290 (FIG. 12), may configure an
associated processor to perform the method 1400.
[0418] At 1402, data from the sleep system 100 is received from the
sleep system via a communication subsystem 1270, 1370. The received
data may be raw data (e.g. sensor samples) from the sleep system
100 sensors or it may be data which was previously processed, such
as mattress health information, sleep state information and/or
sleep environment information.
[0419] Optionally, in some embodiments, at 1404 the received data
may be processed. The nature of this processing may depend on the
form that the data is received in (e.g. whether processing has
already been performed on the data by another system such as the
sleep system 100). For example, where raw data is received or where
data is received that has not been fully processed, processing may
be performed to determine information included in the display
screen(s) 1500, 1600, 1700, 1800, 1900, 2000, 2100.
[0420] Then, at 1406, a display screen 1500, 1600, 1700, 1800,
1900, 2000, 2100 is generated based on either the received data or
the processed data. These display screens may be of the type
described above with reference to FIG. 12 and FIGS. 15 to 21.
[0421] While the embodiments described herein have generally
referred to embodiments in which sensors are embedded in a
mattress, in other embodiments, a mattress sheet or sock could be
used to retrofit a traditional mattress with the components
described herein. For example, the force sensors 120a-120h,
temperature sensor 122 and/or humidity sensor 124 of FIG. 1 could
instead be affixed to a mattress sheet or a sock which is
configured to be applied to a mattress.
[0422] Furthermore, in at least some embodiments, at least some of
the sleep monitoring functions described above may be performed
automatically. That is, the sleep system 100 may perform background
processes which monitor for an occupant's presence. Thus, a user
may not have to actively turn the sleep system on or off.
[0423] The various embodiments presented above are merely examples.
Variations of the innovations described herein will be apparent to
persons of ordinary skill in the art, such variations being within
the intended scope of the present application. In particular,
features from one or more of the above-described embodiments may be
selected to create alternative embodiments comprised of a
sub-combination of features which may not be explicitly described
above. In addition, features from one or more of the
above-described embodiments may be selected and combined to create
alternative embodiments comprised of a combination of features
which may not be explicitly described above. Features suitable for
such combinations and sub-combinations would be readily apparent to
persons skilled in the art upon review of the present application
as a whole. The subject matter described herein and in the recited
claims intends to cover and embrace all suitable changes in
technology.
* * * * *