U.S. patent application number 16/247427 was filed with the patent office on 2019-08-15 for method and apparatus for off-body detection for wearable device.
The applicant listed for this patent is Fitbit, Inc.. Invention is credited to Subramaniam Venkatraman, Kevin Pu Weekly, Shelten Gee Jao Yuen.
Application Number | 20190251238 16/247427 |
Document ID | / |
Family ID | 56079381 |
Filed Date | 2019-08-15 |
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United States Patent
Application |
20190251238 |
Kind Code |
A1 |
Venkatraman; Subramaniam ;
et al. |
August 15, 2019 |
METHOD AND APPARATUS FOR OFF-BODY DETECTION FOR WEARABLE DEVICE
Abstract
A method and apparatus for capacitive off-wrist detection for
wearable device are disclosed. In one aspect, the wearable device
includes one or more biometric sensors including a capacitive
sensor. The method may involve measuring, based on output of the
capacitive sensor, a capacitance value indicative of proximity of
the wearable device to a user. The method may also involve
detecting a change in the capacitance value within a defined time
interval, the change being greater than or equal to a threshold
change indicative of the wearable device not being proximate to the
user's skin. The method may further involve determining that the
wearable device has been removed from the user in response to
detecting that the change in the capacitance value within the
defined time interval is greater than or equal to the threshold
change.
Inventors: |
Venkatraman; Subramaniam;
(Walnut Creek, CA) ; Weekly; Kevin Pu; (San
Leandro, CA) ; Yuen; Shelten Gee Jao; (Berkeley,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Fitbit, Inc. |
San Francisco |
CA |
US |
|
|
Family ID: |
56079381 |
Appl. No.: |
16/247427 |
Filed: |
January 14, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15012607 |
Feb 1, 2016 |
10181021 |
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16247427 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 20/3278 20130101;
H04L 63/0492 20130101; H04L 63/0861 20130101; G06Q 20/40145
20130101; H04W 4/80 20180201; G06F 19/00 20130101; G06Q 20/354
20130101; G06F 21/35 20130101; G06F 21/32 20130101; G16H 40/63
20180101 |
International
Class: |
G06F 21/32 20060101
G06F021/32; H04W 4/80 20060101 H04W004/80; G16H 40/63 20060101
G16H040/63; G06F 21/35 20060101 G06F021/35; G06Q 20/40 20060101
G06Q020/40; G06Q 20/34 20060101 G06Q020/34; G06Q 20/32 20060101
G06Q020/32; H04L 29/06 20060101 H04L029/06 |
Claims
1. A method of operating a wearable device, the wearable device
capable of being worn by a user and comprising one or more
biometric sensors including an optical sensor configured to emit
light and to detect light, the method comprising: emitting light
from the optical sensor; determining whether an output signal of
the optical sensor is at or below an optical threshold indicative
of the wearable device not being proximate to the user's skin while
the light is being emitted from the optical sensor; determining,
based at least on determining that the output signal is at or below
the optical threshold, whether the output signal is representative
of a cardiac signal; and determining that the wearable device is
not being worn by the user based at least on determining that the
output signal sensor is at or below the optical threshold and
determining that the output signal is not representative of the
cardiac signal.
2. The method of claim 1, further comprising de-authenticating the
user from the wearable device, the user having previously been
authenticated to the wearable device, based at least on determining
that the wearable device is not being worn by the user.
3. The method of claim 2, wherein the de-authenticating of the user
from the wearable device comprises preventing the wearable device
from being used for a mobile payment.
4. The method of claim 2, wherein the de-authenticating of the user
from the wearable device comprises preventing the wearable device
from being used for at least one of: automated teller machine (ATM)
transactions; keyless entry into a vehicle; keyless starting of a
vehicle; keyless entry through a door; opening of a lock; execution
of an electronic signature; unlocking of a computer; automatic
logging into a website; exchange of social network information;
exchange of contact information; disarming of a security system;
automatic upload of biometric data to an online user account
associated with the user; and altering of preferences of a
thermostat.
5. The method of claim 1, wherein: the optical sensor is further
configured to emit infrared light, and emitting light from the
optical sensor further comprises emitting infrared light from the
optical sensor.
6. The method of claim 1, wherein: the optical sensor is further
configured to emit green light, and emitting light from the optical
sensor further comprises emitting green light from the optical
sensor.
7. The method of claim 1, further comprising: determining, based on
the output signal of the optical sensor, at least one
characteristic of the user's skin, and calibrating, based on the
determined at least one characteristic of the user's skin, an
expected amount of light to be received by the optical sensor while
the light is being emitted from the optical sensor, wherein the
detecting whether the output signal of the optical sensor is at or
below the optical threshold and the determining whether the output
signal is representative of a cardiac signal are both further based
on the calibrating.
8. The method of claim 1, further comprising authenticating the
user with the wearable device based at least on receiving from the
user at least one of: a personal identification number (PIN); a
fingerprint; biometric data; facial recognition; a password; and a
pattern match.
9. The method of claim 8, wherein the receiving is by a client
device.
10. The method of claim 1, further comprising: determining that the
wearable device is being worn by the user based at least on either
of: detecting that the output signal is above the optical threshold
and determining that the output signal is representative of the
cardiac signal, and authenticating the user with the wearable
device based at least on determining that the wearable device has
not been removed from the user, and receiving from the user at
least one of: a personal identification number (PIN); a
fingerprint; biometric data; facial recognition; a password; and a
pattern match.
11. The method of claim 1, further comprising ending one or more
operations of the wearable device based at least on determining
that the wearable device has been removed from the user.
12. The method of claim 11, wherein the ending of the one or more
operations comprises putting at least one of the one or more
biometric sensors of the wearable device into a power conservation
mode.
13. The method of claim 1, wherein the determining that the
wearable device has been removed from the user is further based at
least on output received from at least one of: a skin temperature
thermometer; a galvanic skin response sensor; an electromyographic
sensor; and an accelerometer.
14. The method of claim 1, wherein: the one or more biometric
sensors includes a skin temperature thermometer, and the
determining that the wearable device has been removed from the user
is further based at least on output received from the skin
temperature thermometer.
15. The method of claim 1, further comprising de-authenticating the
user from the wearable device based at least on determining that
the wearable device is not within wireless communications range of
a client device.
16. The method of claim 1, further comprising amplifying an output
of the optical sensor to create an amplified signal, wherein the
amplified signal is the output signal of the optical sensor.
17. The method of claim 16, wherein the amplifying further
comprises comparing the amplified signal and a previously sampled
amplified signal.
18. A wearable device capable of being worn by a user, the wearable
device comprising: an optical sensor configured to emit light and
to detect light; at least one processor; and a memory storing
computer-executable instructions for controlling the at least one
processor to: emit light from the optical sensor, determine whether
an output signal of the optical sensor is at or below an optical
threshold indicative of the wearable device not being proximate to
the user's skin while the light is being emitted from the optical
sensor, determine, based at least on determining that the output
signal is at or below the optical threshold, whether the output
signal is representative of a cardiac signal; and determine that
the wearable device is not being worn the user based at least on
the determination that the output signal is at or below the optical
threshold and the determination that the output signal is not
representative of the cardiac signal.
19. The wearable device of claim 18, wherein the optical sensor is
a photoplethysmographic sensor.
20. The wearable device of claim 18, wherein: the optical sensor
further comprises one or more light sources configured to emit one
or more of: green light, infrared light, or light having multiple
wavelengths, and the memory stores further computer-executable
instructions for controlling the at least one processor to emit one
or more of green light, infrared light, or light having multiple
wavelengths light, from the optical sensor.
21. The wearable device of claim 18, wherein the memory stores
further computer-executable instructions for controlling the at
least one processor to de-authenticate the user from the wearable
device, the user having previously been authenticated to the
wearable device, based at least on determining that the wearable
device has been removed from the user.
22. The wearable device of claim 21, wherein the de-authentication
of the user from the wearable device comprises preventing the
wearable device from being used for a mobile payment.
23. The wearable device of claim 21, wherein the de-authentication
of the user from the wearable device comprises preventing the
wearable device from being used for at least one of: automated
teller machine (ATM) transactions; keyless entry into a vehicle;
keyless starting of a vehicle; keyless entry through a door;
opening of a lock; execution of an electronic signature; unlocking
of a computer; automatic logging into a website; exchange of social
network information; exchange of contact information; disarming of
a security system; automatic upload of biometric data to an online
user account associated with the user; and altering of preferences
of a thermostat.
24. The wearable device of claim 18, wherein the memory stores
further computer-executable instructions for controlling the at
least one processor to: determine, based on the output signal of
the optical sensor, at least one characteristic of the user's skin,
and calibrate, based on the determined at least one characteristic
of the user's skin, an expected amount of light to be received by
the optical sensor while the light is being emitted from the
optical sensor, wherein the detecting whether the output signal of
the optical sensor is at or below the optical threshold and the
determining whether the output signal is representative of a
cardiac signal are both further based on the calibration.
25. The wearable device of claim 18, wherein the memory stores
further computer-executable instructions for controlling the at
least one processor to authenticate the user with the wearable
device based at least on receiving from the user at least one of: a
personal identification number (PIN); a fingerprint; biometric
data; facial recognition; a password; and a pattern match.
26. The wearable device of claim 18, wherein the memory stores
further computer-executable instructions for controlling the at
least one processor to: determine that the wearable device is being
worn by the user based at least on either of: detecting that the
output signal is above the optical threshold and determining that
the output signal is representative of the cardiac signal, and
authenticate the user with the wearable device based at least on
determining that the wearable device has not been removed from the
user, and receiving from the user at least one of: a personal
identification number (PIN); a fingerprint; biometric data; facial
recognition; a password; and a pattern match.
27. The wearable device of claim 18, wherein the memory stores
further computer-executable instructions for controlling the at
least one processor to end one or more operations of the wearable
device based at least on determining that the wearable device has
been removed from the user.
28. The wearable device of claim 27, wherein the ending of the one
or more operations comprises putting at least one of the one or
more biometric sensors of the wearable device into a power
conservation mode.
29. The wearable device of claim 18, further comprising at least
one of: a skin temperature thermometer; a galvanic skin response
sensor; an electromyographic sensor; and an accelerometer, wherein
the determination that the wearable device has been removed from
the user is further based at least on output received from at least
one of: a skin temperature thermometer; a galvanic skin response
sensor; an electromyographic sensor; and an accelerometer.
30. The wearable device of claim 18, further comprising a skin
temperature thermometer, wherein the determination that the
wearable device has been removed from the user is further based at
least on output received from the skin temperature thermometer.
31. The wearable device of claim 18, wherein the memory stores
further computer-executable instructions for controlling the at
least one processor to de-authenticate the user from the wearable
device based at least on determining that the wearable device is
not within wireless communications range of a client device.
32. The wearable device of claim 18, further comprising an
amplifier electrically connected to the optical sensor and
configured to amplify an output of the optical sensor to create an
amplified signal, wherein the amplified signal is the output signal
of the optical sensor.
33. The wearable device of claim 32, further comprising: a
sample-and-hold circuit; and a differential or instrumental
amplifier, wherein: the sample-and-hold circuit and differential or
instrumental amplifier are electrically connected to the amplifier,
and differential or instrumental amplifier compares the amplified
signal to a previously sampled amplified signal.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application is a continuation application under 35
U.S.C. .sctn. 120 of U.S. patent application Ser. No. 15/012,607,
filed Feb. 1, 2016, and issued as U.S. Pat. No. 10,181,021 on Jan.
15, 2019, which is hereby incorporated by reference herein in its
entirety.
TECHNICAL FIELD
[0002] This disclosure related to the field of wearable devices,
and particularly, to off-body detection for wearable devices.
BACKGROUND
[0003] Consumer interest in personal health has led to a variety of
personal health monitoring devices being offered on the market.
Such devices, until recently, tended to be complicated to use and
were typically designed for use with one activity, for example,
bicycle trip computers.
[0004] Advances in sensors, electronics, and power source
miniaturization have allowed the size of personal health monitoring
devices, also referred to herein as "biometric tracking,"
"biometric monitoring," or simply "wearable" devices, to be offered
in extremely small sizes that were previously impractical. The
number of applications for these devices is increasing as the
processing power and component miniaturization for wearable devices
improves.
[0005] In addition, wearable devices may be used to authenticate a
user (e.g., via biometric input or a passcode), in order to
authorize a user of the device to perform certain tasks. Such tasks
may include mobile payments, keyless entry, etc. which may be
performed when the user has been authenticated with the wearable
device, but not when the user has been de-authenticated from the
wearable device.
SUMMARY
[0006] The systems, methods and devices of this disclosure each
have several innovative aspects, no single one of which is solely
responsible for the desirable attributes disclosed herein.
[0007] In one aspect, there is provided a method of operating a
wearable device, the wearable device comprising one or more
biometric sensors including a capacitive sensor. The method may
involve: measuring, based on output of the capacitive sensor, a
capacitance value indicative of proximity of the wearable device to
a user; and detecting a change in the capacitance value within a
defined time interval, the change being greater than or equal to a
threshold change indicative of the wearable device not being
proximate to the user's skin. The method may further involve:
determining that the wearable device has been removed from the user
in response to detecting that the change in the capacitance value
within the defined time interval is greater than or equal to the
threshold change; and de-authenticating the user from the wearable
device in response to determining that the wearable device has been
removed from the user.
[0008] In another aspect, there is provided a method of operating a
wearable device, the wearable device comprising one or more
biometric sensors including an optical sensor and a capacitive
sensor. The method may involve: detecting that an output signal of
the optical sensor falls to or below an optical threshold
indicative of the wearable device not being proximate to the user's
skin; measuring, based on output of the capacitive sensor, a
capacitance value indicative of proximity of the wearable device to
the user; and detecting a change in the capacitance value within a
defined time interval that is greater than or equal to a threshold
change indicative of the wearable device not being proximate to the
user's skin. The method may further involve: determining that the
wearable device has been removed from the user in response to at
least one of (i) detecting that the output signal of the optical
sensor has fallen to or below the optical threshold and (ii)
detecting that the change in the capacitance value within the
defined time interval is greater than or equal to the threshold
change; and de-authenticating the user from the wearable device in
response to determining that the wearable device has been removed
from the user.
[0009] In yet another aspect, there is provided a wearable device
that includes a capacitive sensor configured to measure a
capacitance value indicative of proximity of the wearable device to
a user. The wearable device may further include at least one
processor, and a memory storing computer-executable instructions
for controlling the at least one processor to: detect that a change
in the capacitance value within a defined time interval is greater
than or equal to a threshold change indicative of the wearable
device not being proximate to the user's skin; determine that the
wearable device has been removed from the user in response to
detecting that the change in the capacitance value within the
defined time interval is greater than or equal to the threshold
change; and de-authenticate the user from the wearable device in
response to determining that the wearable device has been removed
from the user.
[0010] In still another aspect, there is provided a wearable device
that includes an optical sensor configured to monitor at least one
biometric of a user, and a capacitive sensor configured to measure
a capacitance value indicative of proximity of the wearable device
to the user. The wearable device may further include at least one
processor, and a memory storing computer-executable instructions
for controlling the at least one processor to: detect that an
output signal of the optical sensor falls to or below an optical
threshold indicative of the wearable device not being proximate to
the user's skin; detect that a change in the capacitance value
within a defined time interval is greater than or equal to a
threshold change indicative of the wearable device not being
proximate to the user's skin; determine that the wearable device
has been removed from the user in response to at least one of (i)
detecting that the output signal of the optical sensor has fallen
to or below the optical threshold and (ii) detecting that the
change in the capacitance value within the defined time interval is
greater than or equal to the threshold change; and de-authenticate
the user from the wearable device in response to determining that
the wearable device has been removed from the user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] FIG. 1A is a block diagram illustrating certain components
of an example wearable device in accordance with aspects of this
disclosure.
[0012] FIG. 1B is a block diagram illustrating example biometric
sensors which may be in communication with a processor of a
wearable device in accordance with aspects of this disclosure.
[0013] FIG. 2 is an example of a wrist-worn device in accordance
with aspects of this disclosure.
[0014] FIG. 3 is a perspective view illustrating another example of
a wrist-worn device in accordance with aspects of this
disclosure.
[0015] FIG. 4 is a cross-sectional view of a wearable device
including a capacitive sensor in accordance with aspects of this
disclosure.
[0016] FIG. 5 is a block diagram illustrating example features of a
wearable device, including a capacitive sensor, in accordance with
aspects of this disclosure.
[0017] FIG. 6 is a block diagram illustrating another example of
features of a wearable device, including a capacitive sensor, in
accordance with aspects of this disclosure.
[0018] FIGS. 7A and 7B are graphs illustrating examples of the
capacitance measured by a capacitive sensor in accordance with
aspects of this disclosure.
[0019] FIG. 8 is a cross-sectional view of an example wearable
device that includes an optical sensor in accordance with aspects
of this disclosure.
[0020] FIGS. 9A and 9B are example schematics of circuits used for
a photoplethysmographic (PPG) sensor in accordance with aspects of
this disclosure.
[0021] FIG. 10 is a flowchart illustrating an example method for
off-body detection in accordance with aspects of this
disclosure.
[0022] FIG. 11 is a flowchart illustrating another example method
for off-body detection in accordance with aspects of this
disclosure.
[0023] FIG. 12 is a flowchart illustrating yet another example
method for off-body detection in accordance with aspects of this
disclosure.
DETAILED DESCRIPTION
[0024] The detection of removal a wearable device from a user,
which may be referred to as "off-wrist detection" in the case of a
wearable device worn on the wrist, may be used by a processor of
the wearable device as an input for certain processing routines.
Although the term "off-wrist detection" may be used throughout this
disclosure, the embodiments described herein may generally apply to
the detection of removal of a wearable device from any location of
a user's body. Thus, the present disclosure may equally apply to
the removal of a wearable device from, for example, a user's ankle,
arm, and/or leg. The term "off-wrist detection" is used
interchangeably with the term "off-body detection" and should also
be understood as being applicable to the detection of the removal
of a wearable device from other locations on a user.
[0025] One example processing routine performed in response to
off-wrist detection is the de-authorization of the wearable device
from being used in the performance of certain tasks requiring
authorization. As discussed above, wearable devices may be
authorized by a user to be used as a payment device for mobile
payments. For example, a wearable device may include a near field
communication (NFC) chip which may store the user's credit card,
debit card, and/or bank account information. When authorized, the
user may scan the NFC chip by, for example, moving the wearable
device within close proximity of an NFC reader, in order to make a
payment. In this context, there remains a need to ensure
de-authorization of a user from performing tasks such as mobile
payments when the wearable device is no longer worn by the user in
order to prevent such tasks from being performed by unauthorized
parties.
[0026] Since a wearable device may be tied to a user's financial
information, it is desirable to ensure that the wearable device
cannot be used for mobile payments by an individual who is not
associated with or authorized to use the stored financial
information. This may be accomplished by de-authorizing the device,
thereby preventing the device from being used for mobile payment.
One method of de-authorizing a wearable device is to de-authorize
the wearable device upon off-body detection, for example, when the
user removes the device from his/her body (e.g., wrist). In other
words, the user may be de-authenticated from using the wearable
device to make mobile payments if the processor of the wearable
device determines that the wearable device has been removed from
the user.
[0027] Current approaches to off-wrist detection may not provide
the level of security required for the handling of sensitive data,
such as the type of information used for mobile payments. For
example, certain off-wrist detection techniques may have a delay
from the time the wearable device is removed from a user's wrist
until the wearable device has confirmed the off-wrist event. During
such a delay, it may be possible for a thief or unauthorized user
to attach the wearable device to his/her wrist before the device
has detected the off-wrist event, thereby leaving the wearable
device authorized for mobile payments by the thief. One aspect of
this disclosure provides techniques for ensuring that off-wrist
events can be detected and/or confirmed quickly enough to prevent
unauthorized mobile payments.
[0028] Although the above-description of off-wrist detection
relates to mobile payments, the present disclosure is not so
limited. Other applications for off-wrist detection may include
determining the accuracy or confidence level of measured biometric
signals such as heart rate, galvanic skin response, body
temperature, etc. The accuracy or confidence level may then be used
in excluding certain biometric signal measurements when analyzing
the measured biometric signals, thereby increasing the accuracy and
relevance of the analysis. Accordingly, if it is determined that
the wearable device is off-wrist, then biometric signals measured
after the off-wrist detection may be ignored or discarded, or the
accuracy or confidence level of such signals may otherwise be
flagged as questionable. There are many other applications for
off-wrist detection which may be implemented in conjunction with
this disclosure, such as, for example, keyless entry, electronic
signatures (e-signatures), logging into a computer, etc., as
described in further detail below.
Wearable Device Overview
[0029] FIG. 1A is a block diagram illustrating an example wearable
device in accordance with aspects of this disclosure. The wearable
device 10 may include a processor 120, a memory 130, a wireless
transceiver 140, and one or more biometric sensor(s) 160. The
wearable device 10 may also optionally include a user interface 110
and one or more environmental sensor(s) 150. The wireless
transceiver 140 may be configured to wirelessly communicate with a
client device 20 and/or server 22, for example, either directly or
when in range of a wireless access point (not illustrated). Each of
the memory 130, the wireless transceiver 140, the one or more
biometric sensor(s) 160, the user interface 110, and/or the one or
more environmental sensor(s) 150 may be in electrical communication
with the processor 120. In some embodiments, the wireless
transceiver 140 may include an NFC chip (which may be separately
located from the remaining wireless circuitry of the wireless
transceiver 140) for communication with an NFC reader. The NFC chip
may be a powered or passive device. When the NFC chip is a powered
device, the processor 120 may power off the NFC chip to disable the
NFC chip's wireless communication functionality. In certain
embodiments, the NFC chip may store information for performing
mobile payments/transactions (e.g., credit card, debit card, and/or
bank account information).
[0030] The memory 130 may store instructions for causing the
processor 120 to perform certain actions. For example, the
processor 120 may be configured to detect off-body events based on
instructions stored in the memory 130. The processor may receive
input from the one or more of the biometric sensor(s) 160 and/or
the one or more environmental sensors 150 in order to determine
whether the wearable device 10 has been removed from the user. In
some embodiments, the biometric sensors 160 may include one or more
of a capacitive sensor, an optical sensor (e.g., a
photoplethysmographic (PPG) sensor), an accelerometer, and/or other
biometric sensor(s). Further information regarding such biometric
sensors are described in more detail below (e.g., in connection
with FIG. 1B).
[0031] The wearable device 10 may collect one or more types of
physiological and/or environmental data from the one or more
biometric sensor(s) 160, the one or more environmental sensor(s)
150, and/or external devices and communicate or relay such
information to other devices (e.g., the client device 20 and/or the
server 22), thus permitting the collected data to be viewed, for
example, using a web browser or network-based application. For
example, while being worn by the user, the wearable device 10 may
perform biometric monitoring via calculating and storing the user's
step count using the one or more biometric sensor(s) 160. The
wearable device 10 may transmit data representative of the user's
step count to an account on a web service (e.g., www.fitbit.com),
computer, mobile phone, and/or health station where the data may be
stored, processed, and/or visualized by the user. The wearable
device 10 may measure or calculate other physiological metric(s) in
addition to, or in place of, the user's step count. Such
physiological metric(s) may include, but are not limited to: energy
expenditure, e.g., calorie burn; floors climbed and/or descended;
heart rate; heartbeat waveform; heart rate variability; heart rate
recovery; location and/or heading (e.g., via a global positioning
system (GPS), global navigation satellite system (GLONASS), or a
similar system; elevation); ambulatory speed and/or distance
traveled; swimming lap count; swimming stroke type and count
detected; bicycle distance and/or speed; blood pressure; blood
glucose; skin conduction; skin and/or body temperature; muscle
state measured via electromyography; brain activity as measured by
electroencephalography; weight; body fat; caloric intake;
nutritional intake from food; medication intake; sleep periods
(e.g., clock time, sleep phases, sleep quality and/or duration); pH
levels; hydration levels; respiration rate; and/or other
physiological metrics.
[0032] The wearable device 10 may also measure or calculate metrics
related to the environment around the user (e.g., with the one or
more environmental sensor(s) 150), such as, for example, barometric
pressure, weather conditions (e.g., temperature, humidity, pollen
count, air quality, rain/snow conditions, wind speed), light
exposure (e.g., ambient light, UV light exposure, time and/or
duration spent in darkness), noise exposure, radiation exposure,
and/or magnetic field. Furthermore, the wearable device 10 (and/or
the client device 20 and/or the server 22) may collect data from
the biometric sensor(s) 160 and/or the environmental sensor(s) 150,
and may calculate metrics derived from such data. For example, the
wearable device 10 (and/or the client device 20 and/or the server
22) may calculate the user's stress or relaxation levels based on a
combination of heart rate variability, skin conduction, noise
pollution, and/or sleep quality. In another example, the wearable
device 10 (and/or the client device 20 and/or the server 22) may
determine the efficacy of a medical intervention, for example,
medication, based on a combination of data relating to medication
intake, sleep, and/or activity. In yet another example, the
wearable device 10 (and/or the client device 20 and/or the server
22) may determine the efficacy of an allergy medication based on a
combination of data relating to pollen levels, medication intake,
sleep and/or activity. These examples are provided for illustration
only and are not intended to be limiting or exhaustive.
[0033] FIG. 1B is a block diagram illustrating a number of example
biometric sensors that may be in communication with the processor
of the wearable device in accordance with aspects of this
disclosure. For example, in the embodiment of FIG. 1B, the wearable
device 10 may include a capacitive sensor 300 which may be used for
the detection of off-body events. The wearable device 10 may
further include an optical sensor 500 (e.g., a PPG sensor), and may
optionally include an accelerometer 162 and/or other biometric
sensor(s) 164. Each of the biometric sensors illustrated in FIG. 1B
is in electrical communication with the processor 120 to allow the
processor 120 to determine whether the wearable device 10 has been
removed from a user. The processor 120 may use input received from
any combination of the capacitive sensor 300, the optical sensor
500, the accelerometer 162, and/or the other biometric sensor(s)
164 in detecting an off-body event. In some embodiments, the
capacitive sensor 300, the optical sensor 500, the accelerometer
162, and/or the other biometric sensor(s) 164 may correspond to the
biometric sensor(s) 160 illustrated in FIG. 1A.
[0034] The wearable device 10 according to embodiments and
implementations described herein may have a shape and/or size
adapted for coupling to (e.g., secured to, worn, borne by, etc.)
the body or clothing of a user. FIG. 2 shows an example of a
wrist-worn wearable device 202 in accordance with aspects of this
disclosure. The wrist-worn wearable device 202 may have a display
205, button(s) 204, electronics package (not illustrated), and/or
an attachment band 206. The attachment band 206 may be secured to
the user through the use of hooks and loops (e.g., Velcro), a
clasp, and/or a band having memory of its shape, for example,
through the use of a spring metal band.
[0035] FIG. 3 is a perspective view illustrating another example of
a wrist-worn device in accordance with aspects of this disclosure.
The wrist-worn wearable device 202' of FIG. 3 may include button(s)
204', an attachment band 206', fasteners 208 (e.g., hook and loops,
clasps, or band shape memory), a device housing 210, a sensor
protrusion 212, and/or a charging/mating recess 214 (e.g., for
mating with a charger or data transfer interface of a cable, etc.).
In contrast to the wrist-worn wearable device 202 of FIG. 2, in
FIG. 3, the wrist-worn wearable device 202' includes the sensor
protrusion 212 and the recess 214 for mating with a charger and/or
data transmission cable. FIG. 3 also illustrates the device housing
210 which may house internals of the wrist-worn wearable device
202' such as, for example, the processor 120, the capacitive sensor
300, the optical sensor 500, and/or the accelerometer 162. The
optical sensor 500 may be housed directly below the sensor
protrusion 212. Each of the capacitive sensor 300 and the optical
sensor 500 are described in further detail below in connection with
FIGS. 4 to 9B.
[0036] Certain implementations of off-body detection in accordance
with this disclosure employ the use of a capacitive sensor 300 for
the detection of off-wrist events. Capacitive sensors are utilized
in applications such as capacitive touchscreens and radio frequency
(RF) power modulation for tablets and smartphones. However, in the
context of wearable devices 10 (e.g., smartwatches and activity
trackers), capacitive sensors, which may be used to detect the
human body, may be required to detect a much smaller capacitance
change than in other applications of capacitive sensors. In
addition to the size of the sensing electrode required to be
integrated within a capacitive sensor 300 into the body of a
wearable device 10, manufacturing and aesthetic considerations of
the wearable device 10 may limit how close the internal sensing
electrode of the capacitive sensor 300 may be positioned with
respect to the human body when the wearable device 10 is worn.
Wearable devices 10 may also experience a high amount of motion,
particularly when the user is exercising. This may introduce
variability into capacitive off-body detection as this motion may
result in large transients in the capacitive sensor output (see,
e.g., FIGS. 7A and 7B and the related description below). The
magnitude of these transients may approach the normal signal
deflection of a device being taken off the body, and thus the
motion-induced noise in the capacitive sensor output may be
incorrectly determined or interpreted to be an off-body event. This
variability may be accounted for by certain aspects of the present
disclosure, including algorithms (see, e.g., FIG. 10 and the
related description) and information from optical sensor(s) (e.g.,
the optical sensor 500) in order to augment the off-body detection
with additional information, and thereby improve the accuracy of
the on/off-body detection of the wearable device 10.
Capacitive Sensor
[0037] FIG. 4 is a cross-sectional view of a portion of an example
wearable device that includes a capacitive sensor in accordance
with aspects of this disclosure. As shown in FIG. 4, the capacitive
sensor 300 may be housed within a device housing 210. The device
housing 210 may also be referred to herein as a device exterior or
device body. The capacitive sensor 300 may include a capacitive
sensor electrode 310, an optional active shield 320, and a ground
plane 330. When the wearable device 10 is worn by a user, the
device exterior 210 may be placed adjacent to the user's skin
350.
[0038] FIG. 5 is a block diagram illustrating certain components of
another example wearable device that includes a capacitive sensor
in accordance with aspects of this disclosure. With reference to
FIG. 5, the capacitive sensor 300 may include a capacitive sensor
electrode 310, a capacitance to digital converter (CDC) 315, an
optional active shield 320, an optional current source 325, and a
ground plane 330. Although the capacitive sensor 300 may be
referred to for convenience as including each of the capacitive
sensor electrode 310, the CDC 315, the active shield 320, the
ground plane 330, etc., this disclosure is not so limited. For
example, the active shield 320, current source 325, and ground
plane 330 may be considered separate elements from the capacitive
sensor 300 or may be omitted from the wearable device 10
entirely.
[0039] FIG. 6 is a block diagram illustrating certain components of
yet another example wearable device that includes a capacitive
sensor in accordance with aspects of this disclosure. With
reference to FIG. 6, the capacitive sensor 300 may include a
capacitive sensor electrode 310, a CDC 315, and an optional
capacitive sensor driver 340. The CDC 315 may drive the capacitive
sensor electrode 310 and receive an output signal from the
capacitive sensor electrode 310. The CDC 315 may output a digital
signal representative of capacitance measured by the capacitive
sensor electrode 310 to the processor 120. Although FIGS. 4 to 6
illustrate a number of different components and configurations of
the capacitive sensor, any combination of the illustrated
components may be included in other implementations of the
capacitive sensor 300.
[0040] Returning to FIG. 4, the device exterior 210 may be
interposed between the user's skin 350 and the capacitive sensor
300. Accordingly, the capacitive sensor 300 may be protected from
the external environment via the device exterior 210. The
capacitive sensor plate or electrode 310 may be located adjacent to
the device exterior 210 in order to be capacitively coupled with
the user's skin 350. In one embodiment, the sensor electrode 310 is
constructed via forming a copper area on a printed circuit board
(PCB) internal to the wearable device 10 and the copper area may be
placed to be close to the device exterior 210. The capacitive
sensor electrode 310 may experience a change in capacitive value
based on the proximity of the user's skin 350 to the capacitive
sensor electrode 310. The active shield 320 may be located over the
capacitive sensor electrode 310, and the ground plane 330 may be
located over the active shield 320. The ground plane 330 shields
the capacitive sensor electrode 310 from the other components of
the wearable device 10, such as the processor 120 and/or other
biometric sensor(s) 160. As such, the ground plane 330 may suppress
the effects of electrical noise generated by electrical components
of the wearable device 10 from interfering with the capacitance
measurement performed by the capacitive sensor 300. The ground
plane 330 may also protect the other components of the wearable
device 10 from electrical noise radiated from the capacitive sensor
electrode 310, for example, during a capacitive measurement.
[0041] The dashed arrows shown in FIGS. 5 and 6, illustrate
capacitive coupling between the various components. For example,
the user's skin may be capacitively coupled with the capacitive
sensor electrode 310 when within a certain distance from the
capacitive sensor electrode 310. Further, each of the active shield
320 and the ground plane 330 may be capacitively coupled to the
capacitive sensor electrode 310. With reference to FIGS. 4 and 5,
certain operations performed by the CDC 315 may be impaired by the
ground plane 330 being located near the sensor electrode 310. As
such, in at least one embodiment, the active shield 320 may be
interposed between the capacitive sensor electrode 310 and the
ground plane 330. The current source 325, which may be integrated
into the CDC 315 in certain embodiments, may drive the active
shield 320 to substantially the same potential as the capacitive
sensor electrode 310. This may effectively remove capacitive
coupling between the capacitive sensor electrode 310 and the ground
plane 330, thereby allowing the capacitive sensor 300 to have
higher measurement sensitivity.
[0042] Referring to FIG. 6, the capacitive sensor 300 may measure a
capacitive value that is indicative of the proximity of a
conductive object, such as the user's body 350, to the capacitive
sensor electrode 310. In one implementation, the capacitive sensor
300 may include the capacitive sensor driver 340 that drives the
capacitive sensor electrode 310 by applying a defined amount of
charge to the capacitive sensor electrode 310. In certain
implementations, the capacitive sensor driver 340 may be
incorporated into the CDC 315. The CDC 315 may measure the
capacitance of the capacitive sensor electrode 310 in response to
the capacitive sensor driver 340 driving the capacitive sensor
electrode 310. The processor 120 may receive the measured
capacitance from the CDC 315.
[0043] The capacitance of the capacitive sensor electrode 310 may
change in response to the conductive object being moved closer or
further from the capacitive sensor electrode 310. Accordingly, the
processor 120 may be configured to detect the proximity of the
conductive object (e.g., the user's skin) in response to a change
in capacitance measured by the capacitive sensor 300. The
capacitance measured by the capacitive sensor 300 increases as the
conductive object moves or is brought closer to the sensor
electrode 310 and decreases when the conductive object moves or is
taken away from the sensor electrode 310. There may be challenges
in measuring or calibrating absolute values of capacitance measured
by the capacitive sensor 300, which may represent on-wrist and
off-wrist states, due to the dynamic nature of the dielectric
material properties which may affect the capacitance measured by
the capacitive sensor 300. For example, the dielectric material
properties of the air or environment, the device exterior 210,
and/or any glues used to manufacture the wearable device 10 may
each have an effect on the capacitance measured by the capacitive
sensor 300. Furthermore, there may be manufacturing and/or
day-to-day variations (e.g., variations in temperature and
humidity) which may significantly impact capacitance
measurements.
[0044] FIGS. 7A and 7B are graphs illustrating example capacitance
values that may be measured by a capacitive sensor in accordance
with aspects of this disclosure. The variations in the measured
capacitance are illustrated by the fluctuations in the capacitance
value. One method of distinguishing changes in the measured
capacitance that are due to typical variations in the capacitance
from those changes that are due to the wearable device 10 being
removed from the user's body is to identify short-term changes in
the measured capacitance, such as those that are generated when a
conductive object (e.g., the skin on a user's wrist) is suddenly
brought close or taken away from the device.
[0045] In one embodiment, these short-term changes in the
capacitance may be identified by the processor 120 by applying a
low-pass filter to the measured capacitance and then comparing any
changes in the filtered signal to a threshold value. A low-pass
filter may be used to remove (e.g., attenuate) high frequency
signals generated due to noise or other high frequency fluctuations
in the measured capacitance. Thus, the signal remaining after
applying the low-pass filter to the measured capacitance may be a
more accurate representation of the capacitance due to proximity of
the wearable device 10 to the user's body. The threshold value may
be an off-wrist threshold value that is indicative of the wearable
device 10 not being proximate to the user's body. For example, the
off-wrist threshold value may be a capacitance value that is set
based on capacitive measurements taken when the wearable device 10
is not being worn by the user.
[0046] In another embodiment, the processor 120 may detect a rate
of change (e.g., a derivative) of the measured capacitance and
compare the rate of change to a threshold rate. In this embodiment,
the rate of change being greater than the threshold rate may
indicate that the wearable device 10 has been removed from the
user's body. For example, a negative rate of change in the measured
capacitance value that is greater than a defined threshold over a
defined interval (see the change in the measured capacitance
between time t.sub.2 and t.sub.3 in FIG. 7A) may be representative
of the wearable device 10 being removed from the body of the
user.
[0047] In yet another embodiment, the processor 120 may track a
moving baseline of the measured capacitance and set threshold
values as levels relative to the moving baseline which indicate
whether the measured capacitance is indicative of an off-wrist
event. In some embodiments, the processor 120 may track a baseline
capacitance value by low-pass filtering the raw input signal
received from the CDC 315. In some embodiments, the processor 120
may set an on-wrist baseline value and an off-wrist baseline value
relative to the moving baseline. The processor 120 may update the
on-wrist and an off-wrist baseline values in response to, for
example, the average value of the moving baseline changing over
time. For example, after the user has worn the wearable device 10
for a period of time, the capacitance measured by the capacitive
sensor 300 may increase, thereby increasing the average of the
moving baseline. The processor 120 may update the on-wrist baseline
value in response to the increase in the capacitance measured by
the capacitive sensor 300 to reflect this change in the measured
capacitance. The average of the measured capacitance may be
determined over a defined period of time.
[0048] The processor 120 may set the on-wrist baseline value as a
value which is indicative of the wearable device 10 being worn by
the user and may set the off-wrist baseline value as a value which
is indicative of the wearable device 10 not being worn by the user.
In some embodiments, the processor 120 may determine that the
wearable device 10 is being worn by or otherwise on the user in
response to the moving baseline being closer to the on-wrist
baseline value. Similarly, the processor 120 may determine that the
wearable device 10 has been removed from the user in response to
the moving baseline being closer to the off-wrist baseline value.
In other embodiments, the processor 120 may set threshold values
corresponding to each of the on-wrist and off-wrist baseline values
that are within a defined percentage deviation from the respective
baseline values. The processor 120 may determine that the wearable
device 10 has been removed from the user in response to determining
that the moving baseline has deviated from the on-wrist baseline
value by more than the defined percentage (e.g., the moving
baseline has crossed the threshold value corresponding to a
deviation from the on-wrist baseline value). The processor 120 may
determine that the wearable device 10 is being worn by or otherwise
on the user in response to determining that the moving baseline has
deviated from the off-wrist baseline value by more than the defined
percentage (e.g., the moving baseline has crossed the threshold
value corresponding to a deviation from the off-wrist baseline
value).
[0049] The processor 120 may also employ the capacitive sensor 300
to detect initial on-wrist event(s) prior to authentication of the
wearable device 10 (described in further detail below). In other
embodiments, the processor 120 may obtain a short-term deflection
level (e.g., a change in capacitance over a defined time interval)
of the input signal received from the CDC 315 by high-pass or
band-pass filtering the signal and setting fixed positive and
negative thresholds with which to compare the filtered signal. For
example, a high-pass or band-pass filter may be used to remove or
attenuate low frequency signals caused due to drift of the baseline
signal and will pass the higher frequency signal caused due to
changes in the proximity of the wearable device 10 to the user's
body. It is noted that the band-pass filter may also remove higher
frequency noise in the signal that may otherwise produce false
positives. The processor 120 may then determine on-wrist or
off-wrist events based on the comparison of the filtered signal
with the fixed positive and negative thresholds.
[0050] FIG. 7A illustrates one example of the capacitance values
that may be measured by a capacitive sensor. As discussed above,
the capacitance value output from the CDC 315 may vary due to
factors other than the wearable device 10 being removed from the
user, such as, for example, due to variations in
temperature/climate or movement of the sensor electrode 310 with
respect to the user due to natural movement the wearable device 10
during operation (e.g., movement of the user's wrist may cause the
wearable device 10 to move further away or closer to the user's
wrist due to momentum of the wearable device 10). Examples of such
changes in capacitance are depicted as the changes in the measured
capacitance before time t.sub.5 and after time t.sub.6 in FIG.
7A.
[0051] Between times t.sub.5 and t.sub.6 of FIG. 7A, the measured
capacitance may have a relatively large drop in value due to the
wearable device 10 being removed from the user. The processor 120
may be configured to detect the change in measured capacitance
between times t.sub.5 and t.sub.6 as indicating that the wearable
device 10 has been removed from the user, and also to detect that
the change in measured capacitance at other times (e.g., between
times t.sub.2 and t.sub.3) as not indicating that the wearable
device 10 has been removed from the user, by comparing changes in
the measured capacitance over a defined time period. For example,
the change in measured capacitance between the peak at time t.sub.1
and the trough at time t.sub.4 may in some cases have a magnitude
that is similar to the change in measured capacitance associated
with an off-wrist event (e.g., between times t.sub.5 and t.sub.6).
However, since the change in measured capacitance between the peak
at time t.sub.1 and trough at time t.sub.4 is over a greater period
of time than the defined time period (e.g., the time period between
t.sub.2 and t.sub.3 and the time period between t.sub.5 and
t.sub.6) the processor 120 may be able to distinguish this change
in capacitance from a change in capacitance that is indicative of
an off-wrist event (e.g., the wearable device 10 being removed from
the user).
[0052] In contrast, the change in measured capacitance between
times t.sub.5 and t.sub.6 may be greater than a threshold change
within the defined interval. Since the change in measured
capacitance between times t.sub.5 and t.sub.6 is within the defined
time interval, the processor 120 may be able to determine that this
change in measured capacitance is indicative of the wearable device
10 being removed from the user.
[0053] FIG. 7B illustrates another example of the capacitance
values that may be measured by the capacitive sensor. In this
example, the off-wrist event may occur near a peak in the measured
capacitance value. As such, the measured capacitance values from
times t.sub.3' to t.sub.4' may not fall below the trough at time
t.sub.2'. As such, in the example of FIG. 7B, a simple threshold
capacitance value (in contrast to a change in the measured
capacitance within a defined interval) may not accurately indicate
whether the wearable device 10 has been removed from the user since
such a threshold capacitance value would not be able to distinguish
between the trough at time t.sub.2' and the capacitance value at
time t.sub.4'.
Optical Sensor
[0054] The processor 120 in conjunction with the capacitive sensor
300 (e.g., as illustrated in FIG. 1B) may reliably detect the
presence of a conductive object close to the sensor electrode;
however, the processor 120 may not be able to easily distinguish
between a human body and an inanimate object (e.g., a metal desk).
Accordingly, in certain embodiments, an optical sensor 500 such as
a PPG sensor 500 may be utilized to supplement the measurements
provided by the capacitive sensor 300. The term "optical sensor"
may be used interchangeably with PPG sensor hereinafter; however,
in certain embodiments, the optical sensor may comprise a non-PPG
sensor. For example, the optical sensor 500 may function as a
proximity sensor that measures proximity to an object such as a
reflective surface (e.g., a user's wrist) based on the luminance
and/or amplitude of reflected light from the object.
[0055] In one example, the PPG sensor 500 may detect a cardiac
signal that may be indicative of the measured capacitance being due
to the presence of a human body (rather than an inanimate object,
for example). The processor 120 may be configured to determine that
when the measured capacitance is indicative of a conductive object
being proximate to the capacitive sensor electrode 310 and the
cardiac signal is present in the output from the PPG sensor 500, a
human is wearing the wearable device 10. The use of a PPG sensor
500 for identifying a cardiac signal (e.g., in connection with
heart rate monitoring) is well understood by those skilled in the
art, and will not be described in greater detail herein. In some
embodiments, the processor 120 may utilize a heart rate tracking
algorithm in connection with a heart rate monitoring function of
the PPG sensor 500 to provide further confidence about whether the
wearable device 10 is on or off wrist (e.g., by confirming that a
heart rate sampled based on output from the PPG sensor 500 is
within an expected heart rate range).
[0056] FIG. 8 is a cross-sectional view of a portion of a wearable
device that includes an optical sensor in accordance with aspects
of this disclosure. In the embodiment of FIG. 8, the optical sensor
500 may be implemented as a PPG sensor 500. The PPG sensor 500 may
be formed within the device body 210 and may include one or more
light sources (e.g., LEDs) 515, a photodetector 520, a PCB 525, and
an optically transparent layer 530. The optically transparent layer
530 may be attached to the device body 210 via a pressure-sensitive
adhesive 510 and a liquid gasket 505 may be provided to seal the
wearable device 10.
[0057] In the embodiment of FIG. 8, the two light sources 515 may
be placed on either side of the photodetector 520 to facilitate PPG
sensing. The number of light sources 515 may vary in other
implementations. Depending on the embodiment, the light sources 515
may emit green light, infrared light, or light having multiple
wavelengths (e.g., red, green, and infrared light or any
combination thereof). In certain embodiments, a light-blocking
material (not illustrated) may be placed between the light sources
515 and the photodetector 520 to prevent any light from the light
sources 515 from reaching the photodetector 520 without first
exiting the body of the wearable device 10. An optically
transparent layer 530 may be placed on the lower surface of the PPG
sensor 500 to form a seal. Although the optically transparent layer
530 is illustrated as being flush with the device body 210, in
other embodiments, the optically transparent layer 530 may form a
protrusion as shown in FIG. 3. The optically transparent layer 530
may serve other functions such as preventing liquid or debris from
entering the wearable device 10 where the light source(s) 515 or
the photodetector(s) 520 are placed. The optically transparent
layer 530 may be formed through in-mold labeling (IML). The light
source(s) 515 and the photodetector(s) 520 may be placed on the PCB
525, which may be flexible in certain embodiments.
[0058] The configuration of FIG. 8 may improve the efficiency of
light flux coupling between the components of the optical sensor
500 and the user's body. For example, in one embodiment, the light
source(s) 515 and/or the associated detector(s) 520 may be disposed
on a flexible or pliable substrate, such as PCB 525, that may flex,
allowing the skin-side of the wearable device 10, which may be made
from a compliant material, to conform (e.g., without additional
processing) or be capable of being shaped (or compliant) to conform
to the shape of the body part (e.g., the user's wrist, arm, ankle,
and/or leg) to which the wearable device 10 is coupled to or
attached during normal operation so that the light source(s) 515
and/or the associated detector(s) 520 is/are close to the skin of
the user (e.g., with little to no gap between the skin-side of the
device and the adjacent surface of the skin of the user).
[0059] In one embodiment, the light source(s) 515 and/or the
associated detector(s) 520 may be disposed on a Flat Flex Cable
(FFC) or flexible PCB 525. In one aspect, the flexible or pliable
substrate (e.g., an FFC or flexible PCB 525) may connect to a
second substrate (e.g., PCB) within the device having other
components disposed thereon (e.g., the data processing circuitry).
Optical components of differing heights may be mounted to different
portions or protrusions of flexible substrate and pressed or
secured to the housing surface such that the optical components are
flush to the housing surface. In another aspect, the second
substrate may be a relatively inflexible or non-pliable substrate,
fixed within the device, having other circuitry and/or component(s)
(passive and/or active) disposed thereon.
[0060] In related aspects, the processor 120 of the wearable device
10 (e.g., illustrated in FIGS. 1A, 1B and 6) may calibrate the
optical sensor 500 based on the output of the optical sensor 500.
For example, the processor 120 may determine at least one
characteristic of the user's skin (e.g., the user's skin color)
based on the output of the optical sensor 500. The processor 120
may calibrate an expected amount of light to be received by the
photodetector 520 based on the characteristic(s) of the user's
skin. For instance, a user with darker skin tone will be associated
with a greater absorption of green light as measured from an
emitter to a detector of an optical (e.g., PPG) sensor when the
sensor is positioned close to the user's skin. Thus, detection of
"on" and "off" wrist events may then be calibrated relative to the
user's skin color response to the optical sensor.
[0061] In further related aspects, the PPG circuitry may include
amplification circuitry optimized to obtain quality signals
regardless of environmental conditions including, but not limited
to, motion, ambient light, and skin color. Two examples of such PPG
amplification circuitry are described in connection with FIGS. 9A
and 9B.
[0062] FIG. 9A illustrates an example schematic of a
sample-and-hold circuit and differential/instrumentation amplifier
which may be used in PPG sensing. The example circuitry 600 of FIG.
9A may include a photodetector 520, a feedback reactance 610, an
amplifier 620 (e.g., a differential amplifier), a sample-and-hold
circuit 630 (e.g., a buffer), and a differential or instrumental
amplifier 640. The output of the photodetector 520 may be connected
to first input of the amplifier 620 (e.g., the negative terminal)
to be compared with a ground signal (or another signal) connected
to a second input of the amplifier 620 (e.g., the positive
terminal). The output of the amplifier 620 may be connected to the
same input (e.g., the first input) of the amplifier as the
photodetector 520. The output of the amplifier may also be
connected to the sample-and-hold circuit 630 and a first input of
the differential/instrumentation amplifier 640 (e.g., the positive
terminal). The output of the sample-and-hold circuit 630 may also
be connected to a second input of the differential/instrumental
amplifier 640 (e.g., the negative terminal). The
differential/instrumental amplifier 640 may then output a
comparison between the amplified photodetector 520 output and a
previously sampled amplified photodetector 520 output. The output
signal from the circuit 600 may therefore be an amplified
difference between a current sample and a previous sample of the
photodetector 520, referenced to a given voltage.
[0063] FIG. 9B illustrates an example schematic of a circuit for a
PPG sensor using a controlled current source to offset "bias"
current prior to a transimpedance amplifier. The circuit 600' of
FIG. 9B may include a photodetector 520, a current source 650, a
feedback impedance 610', and an amplifier 620' (e.g., a
differential amplifier). The output of the photodetector 520 may be
combined with the output of the current source 650 and then
supplied to a first input of the amplifier 620' (e.g., the negative
terminal). A second input of the amplifier 620' (e.g., the positive
terminal) may be connected to ground or another potential. The
output signal from the amplifier 620' may be fed back to the first
input of the amplifier 620' connected to the photodetector 520 via
the loop with the feedback impedance 610'. This arrangement of
circuit components may allow for a greater gain to be applied at
the transimpedance amplifier stage.
Example Flowchart for Determining Occurrence of Off-Wrist Event
[0064] An optical sensor configured for off-wrist detection, such
as the PPG sensor 500 described herein, may be designed to detect
the proximity and cardiac content in the vicinity of the optical
sensor 500 to detect a human wrist. However, in certain
circumstances the optical sensor 500 may not be able to easily
distinguish between a cardiac signal and an inanimate object that
is in relative motion to the optical sensor 500 when the motion
contains frequencies within the range of heart rate. For example,
when the optical sensor 500 is placed into a bag or pocket of a
walking subject, the processor 120 may mistake the output of the
optical sensor 500 due to the motion of the bag or clothing fabric
as a cardiac signal. Accordingly, in at least one embodiment, the
outputs from a capacitive sensor 300 and an optical sensor 500 may
be considered and/or combined to provide a more accurate detection
of an off-wrist event by determining whether the object being
sensed is conductive (e.g., a human body) or not conductive (e.g.,
a thin fabric).
[0065] FIG. 10 is a flowchart illustrating an example method
operable by a wearable device 10, or component(s) thereof, for
off-body detection in accordance with aspects of this disclosure.
For example, the steps of method 700 illustrated in FIG. 10 may be
performed by a processor 120 of the wearable device 10 or an entity
in communication with the wearable device. For example, the
wearable device 10 may be in communication with a client device 20
(e.g., mobile phone, etc.) which can perform the method 700 or
portions thereof. For convenience, method 700 is described as
performed by the processor 120 of the wearable device 10.
[0066] The method 700 begins at block 701. At block 705, the
processor 120 may detect a change (e.g., a change in a magnitude)
of a measured capacitance value of a capacitive sensor electrode
within a defined time interval. The defined time interval may be
selected based on the time scale associated with removing a
wearable device from a wrist. The time interval may be short enough
such that variations in the measured capacitance due to variables
other than off-wrist events (e.g., changes in temperature) measured
within the time interval are less than a threshold change which is
associated with off-wrist events measured within the time
interval.
[0067] At block 710, the processor 120 may determine whether the
change in the measured capacitance value is greater than a
threshold change. When the change in the measured capacitance value
is not greater than the threshold change, the measured change may
not be indicative of an off-wrist event and the method 700 returns
to block 705. When the change in the measured capacitance value is
greater than the threshold change, the change in capacitance may be
indicative of an off-wrist event, e.g., may be a potential
off-wrist event, and the method 700 proceeds to block 715.
[0068] At block 715, the processor 120 may perform the optional
step of monitoring an output signal of an optical sensor 500. For
example, the processor 120 may monitor, based on output of the
optical sensor 500, at least one characteristic of a heartbeat
waveform of a user of the wearable device 10. In particular, by
analyzing output from the optical sensor 500, the processor 120 may
be able to verify whether the output from the optical sensor 500 is
consistent with the detected potential off-wrist event. The method
700 then proceeds to optional step 720, at which the processor 120
may determine whether the output signal of the optical sensor 500
is representative of a cardiac signal. For example, the processor
120 may determine that the at least one characteristic of the
user's heartbeat waveform is not representative of a cardiac
signal. When the output signal of the optical sensor 500 is
representative of a cardiac signal, the processor may determine
that the potential off-wrist event was not accurate, and the method
700 returns to step 705. When the output signal of the optical
sensor 500 is not representative of a cardiac signal, the processor
120 may determine that the potential off-wrist event was accurate
and the method 700 proceeds to block 725. For example, the
processor 120 may determine that the wearable device has been
removed from the user in response to determining that the at least
one characteristic of the user's heartbeat waveform is not
representative of a cardiac signal. At block 725, the processor 120
determines that the wearable device 10 is not proximate to a user's
wrist, e.g., that the wearable device has been removed from a user.
The method ends at block 730.
[0069] Although the method 700 of FIG. 10 was described as first
analyzing the capacitive sensor 300 output and then confirming an
off-wrist event by analyzing the output of the optical sensor 500,
the method 700 may be performed in the reverse order. For example,
the method 700 may first perform steps 715 and 720 before steps
705, 710, and 725. For example, if the processor 120 determines
that the output signal of the optical sensor 500 is not
representative of a cardiac signal (see step 720), then this may be
indicative of an off-wrist event. The processor may then proceed to
perform steps 705 and 710, and if the processor determines that a
change in the measured capacitance value is greater than a
threshold change (see step 710), then the processor 120 may
determine that the off-wrist event did occur, and the wearable
device 10 is not proximate to a user's wrist (see step 725).
Additionally, in some embodiments, the optical sensor 500 may be
turned off or in a power conservation mode. In these embodiments,
the optical sensor 500 may remain in the off or power conservation
mode until the capacitive sensor identifies a potential off-wrist
event (e.g., as described in steps 705 and 710). Thereafter, the
optical sensor 500 may be engaged to verify the accuracy of the
potential off-wrist event (e.g., as described in steps 715, 720,
and 725). In embodiments where the optical sensor 500 output is
analyzed prior to the capacitive sensor 300, the capacitive sensor
may be turned off or in a power conservation mode (e.g., until the
output signal of the optical sensor 500 is not representative of a
cardiac signal and therefore indicates a potential off-wrist event,
at which point the capacitive sensor 300 may be powered on). The
output from additional sensors may also be analyzed when detecting
off-wrist events. For example, the output of an accelerometer may
be used by the processor 120 in analyzing the output of the optical
sensor 500 and/or the capacitive sensor 300.
[0070] In some embodiments, the processor 120 detects an off-wrist
event by analyzing output from the optical sensor 500 alone,
without regard to capacitive sensor readings. For example, steps
705 and 710 may be omitted from method 700 in FIG. 10. Thus, in
some embodiments, the processor 120 may monitor an output signal of
the optical sensor 500 (step 715), and if the processor 120
determines that the output signal of the optical sensor 500 is not
representative of a cardiac signal (step 720), then processor 120
may determine that the wearable device 10 is not proximate to the
user's wrist (block 725).
[0071] Although not illustrated in FIG. 10, in some embodiments,
the processor 120 may also optionally detect whether the output
signal of the optical sensor 500 falls to or below an optical
threshold indicative of the wearable device 10 not being proximate
to the user's skin, consistent with various techniques described
herein. For example, in some embodiments, this optional step may
occur between steps 715 and 720 or between steps 720 and 725 in the
method 700. Thus, in some embodiments, the processor 120 may
determine that the wearable device 10 is not proximate to the
user's wrist (see step 725), in response to (i) the change in the
measured capacitance value of the capacitive sensor electrode
within the defined time interval being greater than the threshold
change (see step 710), (ii) the output signal of the optical sensor
500 not being representative of a cardiac signal (see step 720),
and/or (iii) the output signal of the optical sensor 500 falling to
or below an optical threshold indicative of the wearable device 10
not being proximate to the user's skin, or any combination of (i),
(ii), and (iii).
[0072] In other embodiments, the processor 120 may also verify the
accuracy of biometric data output from one or more of the biometric
sensors (e.g., the biometric sensor(s) 160 shown in FIG. 1A) after
the wearable device 10 has been determined to have been removed
from a user. For example, after the wearable device 10 has been
determined to have been removed from a user, the processor 120 may
set a flag associated with output received after the detected
removal from one or more of the biometric sensors 160. This flag
may indicate that the output received from the associated one or
more the biometric sensors 160 should be verified for accuracy. The
accuracy of the output received from the one or more biometric
sensors 160 may then be verified by the processor 120 (and/or the
client device 20 and/or the server 22).
[0073] In some implementations, the processor 120 may determine an
on-wrist confidence metric for the measurements associated with
each of the capacitive sensor 300, the optical sensor 500, and/or
other biometric sensors 164, where each confidence metric indicates
a level of confidence or trustworthiness in the accuracy of the
measurements associated with each of the sensors. A first on-wrist
confidence metric, determined based on output of the capacitive
sensor 300, and a second on-wrist confidence metric, determined
based on output of the optical sensor 500, may be used by the
processor 120 to determine whether to use the output of the
capacitive sensor 300 of the output of the optical sensor 500 in
determining that the wearable device 10 has been removed from the
user. For example, the processor 120 may determine a first on-wrist
confidence metric based on the output of the capacitive sensor and
determine a second on-wrist confidence metric based on the output
of the optical sensor. The processor 120 may classify one of the
first on-wrist confidence metric and the second on-wrist confidence
metric as a greater value confidence metric and select one of the
capacitive sensor and the optical sensor associated with the
greater confidence metric. Thus, the processor 120 may determine
that the wearable device has been removed from the user based on
the output of the selected sensor. The first and second on-wrist
confidence metrics may be determined based on, for example, noise
in the corresponding sensor outputs, unexpected patterns in the
corresponding sensor outputs, etc.
Applications for the Determination of Off-Wrist Event
[0074] As discussed above, the determination that the wearable
device 10 has been removed from the body of a user may be used to
de-authenticate the user from the wearable device 10. There are a
number of applications in which the wearable device 10 may use the
determination that the wearable device 10 has been removed from the
user. One application of the authentication and de-authentication
of the wearable device 10 is to add security to the use of the
wearable device 10 in the context of financial transactions.
However, the wearable device 10 may be used for a number of other
transactions or tasks. For example, while the wearable device 10 is
authenticated, the user may use the wearable device 10 for one or
more secure transactions or tasks, including but not limited to
monetary transfer, credit card purchase(s), automated teller
machine (ATM) cash withdrawal or transaction, ATM authentication,
keyless entry into a vehicle, keyless starting of a vehicle,
keyless entry through a door, opening of a lock, execution of an
electronic signature (e-signature), unlocking of a computer,
automatic logging into a web account or website, "friending"
someone in a social network or exchange of social network
information (e.g., Fitbit.RTM., Facebook.RTM., LinkedIn.RTM.),
exchange of contact information, disabling or disarming of a home
or business security system, automatic upload of biometric data to
an online user account associated with the user, and/or altering
the preferences on a thermostat. These examples of transactions or
tasks may be performed by near contact (e.g., NFC) or direct
contact with objects (e.g., the user may touch a door) or by
launching an application on the wearable device 10 that transmits a
signal to the object(s) either directly (peer-to-peer) or
indirectly (client-server via Internet).
[0075] The de-authentication or de-authorization of the wearable
device 10 from performing secure transactions (e.g., monetary
transfer, credit card purchase(s), ATM cash withdrawal or
transaction, ATM authentication, etc.) may be performed by the
processor 120 in accordance with an NFC standard for the
de-authorization of financial transactions. This may include, for
example, the processor 120 powering off an NFC chip of the wearable
device 10. When the NFC chip is powered down, other devices such as
NFC readers are unable to scan or read information from the NFC
chip, and thus, are unable to access information that may be stored
on the NFC chip such as, for example, credit card, debit card,
and/or bank account information. The processor 120 may power up the
NFC chip after the wearable device 10 has been re-authenticated by
an authorized user of the wearable device 10. In related aspects,
the wearable device 10 may employ any other technique known to
those skilled in the art for the de-authorization of financial
transactions performed with the NFC chip and/or the wireless
transceiver 140 of the wearable device 10.
[0076] In one example, a user may be authenticated with a wearable
device 10 when first putting on a wearable device 10 via inputting
a pin or using a biometric identification method. In another
example, the user may be asked to authenticate the wearable device
10 at the time of the first secure transaction or task after
putting on the wearable device 10. In yet another example, the
wearable device may be paired to a client device 20 (e.g., mobile
phone). When the user first wears the wearable device 10, the user
may be asked to authenticate the wearable device 10 via the client
device 20 with a pin, fingerprint, or other biometric data (e.g.,
heart rate signature). Authentication may also be performed from
the client device 20 via, for example, a fingerprint sensor, facial
recognition, pin entry, password entry, or pattern matching (e.g.,
swipe pattern). Thereafter, for the duration of the wearable being
on the user's wrist as determined by the processor 120 based on
measurements from the capacitive sensor 300 and/or the optical
sensor 500 via the techniques disclosed herein, the user may be
authenticated. Once the wearable device 10 is removed and
determined to have been removed from the user based on the
measurements from the capacitive sensor 300 and/or the optical
sensor 500 via the disclosed techniques, the user may be
de-authenticated from the wearable device 10.
[0077] In one example, the wearable device 10 may become
de-authenticated if it is not in close proximity to a client device
20 (e.g., mobile phone) for a specified period of time. For
instance, if the wearable device 10 is not within the Bluetooth
range of the client device 20 for 10 minutes, the wearable device
10 may be de-authenticated regardless of whether the processor 120
has detected an off-wrist event. In another example, the wearable
device 10 may become de-authenticated when the wearable device 10
is turned off.
[0078] The off-wrist detection methods disclosed herein may be used
to ensure that the user has not taken off the wearable device 10,
since removal of the wearable device 10 from the user's body is
detectable as an off-wrist event using the techniques described
herein. Once the wearable device 10 has determined that the
wearable device 10 has been removed from the user, the wearable
device 10 may be de-authenticated, thereby requiring the user to
re-authorize the wearable device 10 to engage in any activities
that require authentication. This authorization technique may be
used, for example, to authorize payments via the wearable device 10
when paired to a credit/debit card. A similar technique may be used
for authorization of other sensitive tasks, such as entry into
locked doors, password access to computers, pin entry for unlocking
a phone, etc. These techniques may also be used for wearable
devices 10 which are shared between multiple users, allowing the
processor 120 to switch between modes of operation depending on the
particular user who is wearing the wearable device 10. This may
also allow biometric data to be correctly associated with the
particular user that was wearing the wearable device 10 when such
data was observed.
[0079] In one implementation, the determination of whether the
wearable device 10 has been removed from the user may be used to
determine when to measure other biometric signals (e.g., heart
rate, heart rate variability, blood oxygenation, body temperature,
galvanic skin response, etc.) or when to end various operations of
the wearable device 10, thereby optimizing power draw. For example,
certain biometric sensors 160 (including but not limited to the
optical sensor 500) may be turned off or put into a power
conservation mode when the wearable device 10 has been removed from
the user. Further, the accuracy of the algorithms for analyzing
output from the biometric sensors 160 may degrade when invalid data
(such as biometric sensor 160 measurements taken when the device is
not worn by the user) is included in the analysis. Therefore, by
accurately detecting off-wrist events, these types of spurious data
measurements may be prevented from being processed or considered by
an algorithm or from being introduced into a user's records. The
analysis of the data from the biometric sensors 160 may also be
used to increase confidence in the on-wrist and off-wrist state in
certain implementations. For example, the biometric sensors 160 may
provide measurements of skin temperature (e.g., via a skin
temperature thermometer), galvanic skin response (e.g., from a
galvanic skin response sensor), electromyography (e.g., from an
electromyographic sensor), motion (e.g., the user registers enough
movement in a time window as measured by an accelerometer), etc.,
one or more of which may be incorporated into the determination of
whether the wearable device 10 has been removed from the user.
[0080] In another implementation, the determination of whether the
wearable device 10 has been removed from the user may be used to
customize the collection and/or analysis of biometric data for the
user. The techniques disclosed herein may also be used to track a
user's on-wrist and off-wrist states over a period of time and send
this information, along with other collected biometric data to an
Internet-connected server (e.g., the server 22). The collected
on-wrist and off-wrist information may be used by the
Internet-connected server or associated processing infrastructure
to improve the accuracy of algorithms used to analyze biometric
data received from the biometric sensor(s) 160 to calculate
metrics, including but not limited to resting heart rate, caloric
expenditure, fitness level, etc. The tracked on-wrist and off-wrist
state periods may also be used within the Internet-connected server
to calculate statistics which track user engagement of the product
(the wearable device 10), including but not limited to how often or
when he/she is wearing the wearable device 10. This information may
be used to tailor specific information (e.g., fitness related
updates, marketing, etc.) to each user to increase his/her
engagement with the wearable device 10 and/or associated services.
This information may also be presented to the user so that he/she
can track his/her engagement with the wearable device 10 and/or
associated services.
[0081] In some implementations, the wearable device 10 may be
shared between a plurality of users and may be configured to track
biometric data for each of the users and/or automatically upload
the biometric data for each of the users to online user accounts
associated with each of the users. Accordingly, in order to
associate measured biometric data with the particular users who
generated the data, each of the users may be authenticated with the
wearable device 10 upon putting on the wearable device 10. After
the wearable device 10 detects an off-wrist event, the wearable
device 10 may de-authenticate the corresponding user such that
biometric data generated thereafter is not associated with the
corresponding user. The wearable device 10 may then prevent further
automatic uploading of biometric data to an online user account
associated with the user until the user is re-authenticated with
the wearable device 10.
Further Example Flowcharts for Determining Off-Wrist Event
[0082] FIG. 11 is a flowchart illustrating another example method
operable by a wearable device 10, or component(s) thereof, for
off-wrist detection in accordance with aspects of this disclosure.
For example, the steps of method 800 illustrated in FIG. 11 may be
performed by a processor 120 of the wearable device 10. In another
example, a client device 20 (e.g., a mobile phone) in communication
with the wearable device 10 may perform at least some of the steps
of the method 800. For convenience, the method 800 is described as
performed by the processor 120 of the wearable device 10.
[0083] In one implementation, the wearable device 10 comprises one
or more biometric sensors 160 including a capacitive sensor 300 and
the processor 120. The method 800 begins at block 801. At block
805, the processor 120 measures, based on output of the capacitive
sensor 300, a capacitance value indicative of proximity of the
wearable device 10 to a user. At block 810, the processor 120
detects a change in the capacitance value within a defined time
interval. When the change is greater than or equal to a threshold
change, the change may be indicative of the wearable device 10 not
being proximate to the user's skin. At block 815, the processor 120
determines that the wearable device 10 has been removed from the
user. The processor 120 may determine that the wearable device 10
has been removed from the user in response to detecting that the
change in the capacitance value within the defined time interval is
greater than or equal to the threshold change. At block 820, the
processor 120 de-authenticates the user from the wearable device 10
in response to determining that the wearable device 10 has been
removed from the user. The method 800 ends at block 825.
[0084] FIG. 12 is a flowchart illustrating yet another example
method for off-wrist detection in accordance with aspects of this
disclosure. The steps illustrated in FIG. 12 may be performed by a
wearable device 10 or component(s) thereof. For example, the method
900 may be performed by a processor 120 of the wearable device 10.
In another example, a client device 20 in communication with the
wearable device 10 may perform at least some of the steps of the
method 900. For convenience, the method 900 is described as
performed by the processor 120 of the wearable device 10.
[0085] In one implementation, the wearable device 10 comprises an
optical sensor 500, a capacitive sensor 300, and a processor 120.
The method 900 begins at block 901. At block 905, the processor 120
detects that an output signal of the optical sensor 500 falls to or
below an optical threshold indicative of the wearable device 10 not
being proximate to the user's skin. At block 910, the processor 120
measures, based on output of the capacitive sensor 300, a
capacitance value indicative of proximity of the wearable device 10
to the user. At block 915, the processor 120 detects that a change
in the capacitance value within a defined time interval is greater
than or equal to a threshold change indicative of the wearable
device 10 not being proximate to the user's skin. At block 920, the
processor 120 determines that the wearable device 10 has been
removed from the user. The processor 120 may determine that the
wearable device 10 has been removed from the user in response to at
least one of (i) detecting that the output signal of the optical
sensor 500 has fallen to or below the optical threshold and (ii)
detecting that the change in the capacitance value within the
defined time interval is greater than or equal to the threshold
change. At block 925, the processor 120 de-authenticates the user
from the wearable device 10 in response to determining that the
wearable device 10 has been removed from the user. The method 900
ends at block 930.
[0086] Although not illustrated in FIG. 12, in some embodiments,
the processor 120 may also optionally detect whether the output
signal of the optical sensor 500 is representative of a cardiac
signal, consistent with various techniques described herein. For
example, in some embodiments, this optional step may occur before
step 920 in the method 900. Thus, in some embodiments, the
processor 120 may determine that the wearable device 10 has been
removed from the user (see step 920), in response to (i) the output
signal of the optical sensor 500 falling to or below an optical
threshold indicative of the wearable device 10 not being proximate
to the user's skin (see step 905), (ii) the change in the
capacitance value within the defined time interval being greater
than or equal to the threshold change indicative of the wearable
device 10 not being proximate to the user's skin (see step 915),
and/or (iii) the output signal of the optical sensor 500 not being
representative of a cardiac signal, or any combination of (i),
(ii), and (iii).
Other Considerations
[0087] Information and signals disclosed herein may be represented
using any of a variety of different technologies and techniques.
For example, data, instructions, commands, information, signals,
bits, symbols, and chips that may be referenced throughout the
above description may be represented by voltages, currents,
electromagnetic waves, magnetic fields or particles, optical fields
or particles, or any combination thereof.
[0088] The various illustrative logical blocks, and algorithm steps
described in connection with the embodiments disclosed herein may
be implemented as electronic hardware, computer software, or
combinations of both. To clearly illustrate this interchangeability
of hardware and software, various illustrative components, blocks,
and steps have been described above generally in terms of their
functionality. Whether such functionality is implemented as
hardware or software depends upon the particular application and
design constraints imposed on the overall system. Skilled artisans
may implement the described functionality in varying ways for each
particular application, but such implementation decisions should
not be interpreted as causing a departure from the scope of the
present disclosure.
[0089] The techniques described herein may be implemented in
hardware, software, firmware, or any combination thereof. Such
techniques may be implemented in any of a variety of devices, such
as, for example, wearable devices, wireless communication device
handsets, or integrated circuit devices for wearable devices,
wireless communication device handsets, and other devices. Any
features described as devices or components may be implemented
together in an integrated logic device or separately as discrete
but interoperable logic devices. If implemented in software, the
techniques may be realized at least in part by a computer-readable
data storage medium comprising program code including instructions
that, when executed, performs one or more of the methods described
above. The computer-readable data storage medium may form part of a
computer program product, which may include packaging materials.
The computer-readable medium may comprise memory or data storage
media, such as random access memory (RAM) such as synchronous
dynamic random access memory (SDRAM), read-only memory (ROM),
non-volatile random access memory (NVRAM), electrically erasable
programmable read-only memory (EEPROM), FLASH memory, magnetic or
optical data storage media, and the like. The techniques
additionally, or alternatively, may be realized at least in part by
a computer-readable communication medium that carries or
communicates program code in the form of instructions or data
structures and that can be accessed, read, and/or executed by a
computer, such as propagated signals or waves.
[0090] Processor(s) in communication with (e.g., operating in
collaboration with) the computer-readable medium (e.g., memory or
other data storage device) may execute instructions of the program
code, and may include one or more processors, such as one or more
digital signal processors (DSPs), general purpose microprocessors,
an application specific integrated circuits (ASICs), field
programmable logic arrays (FPGAs), or other equivalent integrated
or discrete logic circuitry. Such a processor may be configured to
perform any of the techniques described in this disclosure. A
general purpose processor may be a microprocessor; but in the
alternative, the processor may be any conventional processor,
controller, microcontroller, or state machine. A processor may also
be implemented as a combination of computing devices, for example,
a combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration. Accordingly, the term
"processor," as used herein may refer to any of the foregoing
structure, any combination of the foregoing structure, or any other
structure or apparatus suitable for implementation of the
techniques described herein. Also, the techniques could be fully
implemented in one or more circuits or logic elements.
[0091] The techniques of this disclosure may be implemented in a
wide variety of devices or apparatuses, including a wearable
device, a wireless handset, an integrated circuit (IC) or a set of
ICs (e.g., a chip set). Various components, or units are described
in this disclosure to emphasize functional aspects of devices
configured to perform the disclosed techniques, but do not
necessarily require realization by different hardware units.
Rather, as described above, various units may be combined in a
hardware unit or provided by a collection of inter-operative
hardware units, including one or more processors as described
above, in conjunction with suitable software and/or firmware.
[0092] Although the foregoing has been described in connection with
various different embodiments, features or elements from one
embodiment may be combined with other embodiments without departing
from the teachings of this disclosure. However, the combinations of
features between the respective embodiments are not necessarily
limited thereto. Various embodiments of the disclosure have been
described. These and other embodiments are within the scope of the
following claims.
* * * * *
References