U.S. patent application number 09/734099 was filed with the patent office on 2001-06-21 for elderly fall monitoring method and device.
Invention is credited to Jacobsen, Steven C., Petelenz, Tomasz J., Peterson, Stephen C..
Application Number | 20010004234 09/734099 |
Document ID | / |
Family ID | 22657494 |
Filed Date | 2001-06-21 |
United States Patent
Application |
20010004234 |
Kind Code |
A1 |
Petelenz, Tomasz J. ; et
al. |
June 21, 2001 |
Elderly fall monitoring method and device
Abstract
A method and system for recording acceleration and body position
data from elderly or disabled persons. The fall monitoring system
includes signal feature extraction and interpretive methods for
characterizing accelerations and body positions during fall events.
The system can detect health and life threatening fall events in
elderly persons, and can autonomously notify nursing personnel or
family members that the person is in need of immediate
assistance.
Inventors: |
Petelenz, Tomasz J.; (Salt
Lake City, UT) ; Peterson, Stephen C.; (Salt Lake
City, UT) ; Jacobsen, Steven C.; (Salt Lake City,
UT) |
Correspondence
Address: |
Steve M. Perry
THORPE, NORTH & WESTERN, LLP
P.O. Box 1219
Sandy
UT
84091-1219
US
|
Family ID: |
22657494 |
Appl. No.: |
09/734099 |
Filed: |
December 11, 2000 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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09734099 |
Dec 11, 2000 |
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09179668 |
Oct 27, 1998 |
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6160478 |
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Current U.S.
Class: |
340/539.1 ;
128/903; 340/573.1; 340/689; 600/300 |
Current CPC
Class: |
A61B 5/002 20130101;
G08B 21/0453 20130101; Y10S 128/903 20130101; A61B 5/1117 20130101;
A61B 5/024 20130101; G08B 25/006 20130101; A61B 5/6838 20130101;
A61B 5/0022 20130101; A61B 5/6831 20130101; G08B 21/0446 20130101;
G08B 5/223 20130101; A61B 5/7465 20130101; A61B 5/021 20130101;
A61B 5/6826 20130101; G16H 50/20 20180101; G16H 40/67 20180101 |
Class at
Publication: |
340/539 ;
340/573.1; 340/689; 128/903; 600/300 |
International
Class: |
G08B 021/00 |
Claims
What is claimed is:
1. A method for monitoring a person's fall using an accelerometer
included in a personal monitoring device configured to be carried
on the person, having a microprocessor and a memory buffer, wherein
data is stored in the buffer of the personal monitoring device,
comprising the steps of: (a) sampling an output from the
accelerometer indicative of body acceleration and body angle; (b)
detecting whether the body angle is in a steady state indicative of
a fall for at least two seconds; (c) measuring a fall duration by
reading back through the buffer; (d) determining if an uncontrolled
fall has taken place by testing whether the fall duration is less
than a time threshold; and (e) determining whether a severe fall
has occurred by comparing an angular rate of change of the body
angle and an acceleration amplitude change to a severity
threshold.
2. A method as in claim 1, further comprising the step of signaling
a fall via a communications network when a severe fall has taken
place.
3. A method as in claim 1, further comprising the step of
calculating the severity of an uncontrolled fall by comparing
angular rate of change and an amplitude change to a severity
threshold.
4. A method as in claim 1, wherein the step of measuring the fall
duration further comprises using time duration data for measuring
the fall duration using body angle data stored in the buffer.
5. A method as in claim 1, further comprising the step of
determining whether the fall duration is less then a time threshold
of approximately 0.335 seconds, which indicates that the person's
fall is an uncontrolled fall.
6. A method as in claim 1, wherein the step of determining body
angle further comprises the step of determining body angle using an
output from a three-dimensional accelerometer.
7. A method as in claim 6, further comprising the step of
determining the body angle using an output from a three-dimensional
accelerometer that is filtered with a Chebychev filter.
8. A method as in claim 1, further comprising the step of storing
fall data in a ring buffer.
9. A method as in claim 1, further comprising the steps of
providing a sensor coordinate system and transforming the sensor
coordinate system by rotating the sensor coordinate system to align
a gravity vector with a Z-axis.
10. A method as in claim 9, further comprising the step of creating
a transformed Y-axis of the coordinate system which is
perpendicular to the Z-axis of the transformed sensor coordinate
system.
11. A method as in claim 9, further comprising creating a
transformed X-axis of the sensor coordinate system that is
determined by a right-hand rule based on the Y and Z axes of the
transformed coordinate system.
12. A method for monitoring a person's fall using an accelerometer
in a monitoring device carried on the person, which monitoring
device samples the person's body angle and body acceleration,
comprising the steps of: (a) providing a buffer in the monitoring
device and storing body angle and body acceleration data therein;
(b) detecting whether the body angle is in a horizontal steady
state for at least two seconds; (c) measuring a fall duration using
time duration data for the person's body angle stored in the buffer
of the monitoring device; (d) determining whether the fall duration
is less then a time threshold indicating that the fall is an
uncontrolled fall; and (e) determining the severity of the
uncontrolled fall using an angular rate of change of the body angle
and an acceleration amplitude change.
13. A method as in claim 12, further comprising the step of
repeating steps (a)-(d) until an uncontrolled fall is detected.
14. A method as in claim 12, further comprising the step of using a
Cartesian coordinate system with the accelerometer to detect the
rate of change of the body angle.
15. A method as in claim 14, further comprising the step of
converting the Cartesian coordinate system to a polar coordinate
system in order to perform explicit angle calculations.
16. A method as in claim 12, further comprising the step of storing
fall data in a ring buffer.
17. A method as in claim 16, wherein the step of detecting whether
the body angle is in a horizontal steady state for at least two
seconds, further comprises the step of reading back at least
one-half second in the ring buffer to decide if the person is in a
horizontal position.
18. A method as in claim 17, further comprising the step of
comparing body angle data stored in the buffer to a threshold value
to determine if a person is in a horizontal position, wherein the
threshold comprises that at least 50% of body angle data in the
buffer is greater than 50.degree..
19. A method as in claim 17, step of comparing body angle data
stored in the buffer to a threshold value to determine if a person
is in a horizontal position, wherein the threshold comprises that
at least 80% of body angle data in the buffer is greater than
50.degree..
20. A method as in claim 16, further comprising the step of storing
at least 4 seconds of fall data in the ring buffer.
21. A method as in claim 16, further comprising the step of
comparing maximum body angles, in a 0.2 second window of the ring
buffer, to a maximum body angle threshold.
22. A method for monitoring a person's fall using a
single-dimensional accelerometer in a personal monitoring device,
comprising the steps of: (a) detecting whether an acceleration
exceeds a trigger threshold; (b) collecting at least 2 seconds of
additional acceleration data in a buffer after the acceleration
exceeds a trigger threshold; and (c) finding a largest acceleration
sample value in the buffer; and (d) signaling that a fall has taken
place when the largest acceleration sample value exceeds a maximum
threshold.
23. A method as in claim 22, further comprising the step of
detecting whether a mean acceleration within a buffer window
exceeds a maximum mean threshold.
Description
[0001] This application is a continuation-in-part of U.S. patent
application Ser. No. 09/179,668 filed on Oct. 27, 1998,
TECHNICAL FIELD
[0002] This invention relates generally to the field of motion
monitors for patients, and more particularly to an improved monitor
for bio-mechanically characterizing falls in patients and providing
an alarm in case of a fall.
BACKGROUND ART
[0003] Our aging population, improved health care, and an
increasing number of working women create a demand for technologies
allowing older persons to live independent lives. This number, in
the U.S. alone, is estimated at 27 million people and will grow to
50 million by the year 2010. Thus, there are significant needs in
the development of assistive technologies that allow older people
to live alone safely.
[0004] Health-threatening falls are an important epidemiological
problem in a growing segment of the aging population. Studies
indicate that approximately two thirds of accidents in people 65
years of age or older, and a large percentage of deaths from
injuries are due to falls. It has been estimated that approximately
1.6 million hip fracture injuries worldwide in 1990 were due to
falls, and that this number will increase 6.26% by 2050, with the
highest incidences recorded in Northern Europe and North America.
In the elderly, 90% of hip fractures happen at age 70 and older,
and 90% are due to falls. The falls are usually due (80%) to
pathological balance and gait disorders and not to overwhelming
external force (i.e., being pushed over by some force). More than
50% of elderly persons suffer from arthritis and/or orthopedic
impairments, which frequently leads to falls. Specifically prone to
falls are women experiencing a higher percentage of
arthritis-related structural bone changes. It is estimated that
approximately 5% of falls result in fracture and 1% of all falls
are hip fractures. The percentages vary slightly in different
geographical regions (e.g., Japan, Scandinavia), but the consensus
of the available research is that the falls are a significant
epidemiological problem in the growing elderly population.
[0005] Among older people in the U.S. (age 65+) there are
approximately 750,000 falls per year requiring hospitalization due
to either bone fracturing (approx. 480,000 cases) or hip fracturing
(approx. 270,000 cases). The result of such injuries is an average
hospital stay between 2 and 8 days. Assuming the average cost of
$1,000 per hospital day, a total cost of falls in the elderly for
the health care industry can be estimated at three billion dollars
per year. This figure is likely to increase as the older aged
segment of the population increases.
[0006] Falls in elderly people have been recognized as a major
health problem in an aging population. Physical activity patterns,
detecting the occurrence of falls, and recognizing body motion
patterns inevitably leading to falls are not well understood due to
the lack of systems which allow continuous monitoring of patients
in an accurate, convenient, unobtrusive and socially acceptable
manner.
SUMMARY OF THE INVENTION
[0007] This invention provides a method for monitoring a person's
fall using an accelerometer included in a personal monitoring
device configured to be carried on the person. The monitoring
device has a microprocessor and a memory buffer, and data is stored
in the buffer of the personal monitoring device. The first step in
the method is sampling an output from the accelerometer indicative
of body acceleration and body angle. The next step is detecting
whether the body angle is in a steady state indicative of a fall
for at least two seconds. Then the fall duration is measured by
reading back through the buffer. Another step is determining if an
uncontrolled fall has taken place by testing whether the fall
duration is less than a time threshold. The last step is
determining whether a severe fall has occurred by comparing an
angular rate of change of the body angle and an acceleration
amplitude change to a severity threshold.
[0008] In accordance with another aspect of the present invention,
the method monitors a person's fall using a single-dimensional
accelerometer in a monitoring device on the person. The method
includes the step of detecting whether the person's acceleration
exceeds a maximum threshold. The next step is collecting at least 2
seconds of additional acceleration data in a buffer. Another step
is finding a largest acceleration sample value in the buffer. The
final step is signaling that a fall has taken place when that
acceleration sample value exceeds a predetermined maximum
threshold.
[0009] Additional features and advantages of the invention will be
set forth in the detailed description which follows, taken in
conjunction with the accompanying drawings, which together
illustrate by way of example, the features of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 illustrates a fall detection device which provides
fall information to a caregiver;
[0011] FIG. 2 is a block diagram of a fall detection device and the
associated communications networks;
[0012] FIG. 3 represents a body position vector;
[0013] FIG. 4 represents the gravitational acceleration vector in a
sensor coordinate system;
[0014] FIG. 5 illustrates raw and filtered fall acceleration
data;
[0015] FIG. 6 illustrates a polar coordinate system;
[0016] FIG. 7 represents acceleration data in a polar coordinate
system;
[0017] FIG. 8 is a flow diagram of the fall detection method;
[0018] FIG. 9 is a flow diagram of the logic for another embodiment
the fall detection method;
DETAILED DESCRIPTION
[0019] For the purposes of promoting an understanding of the
principles of the invention, reference will now be made to the
exemplary embodiments illustrated in the drawings, and specific
language will be used to describe the same. It will nevertheless be
understood that no limitation of the scope of the invention is
thereby intended. Any alterations and further modifications of the
inventive features illustrated herein, and any additional
applications of the principles of the invention as illustrated
herein, which would occur to one skilled in the relevant art and
having possession of this disclosure, are to be considered within
the scope of the invention.
[0020] The method and system of the present invention allows for
remote monitoring of health-threatening fall events in nursing home
patients or in similar care facilities. It detects serious fall
events and provides rapid notification of emergency help for
elderly or disabled persons who live alone. In addition, the
physical movements of an elderly person can be monitored to
evaluate their overall physical activity.
[0021] FIG. 1 illustrates a monitor 30 which is pager-sized or
smaller for physical activity monitoring. The size of the invention
enables a patient 32 or person to wear the device continually, thus
providing an added measure of security for the elderly or
homebound. A continuously wearable device also increases the "peace
of mind" for their working children, nurses and/or caretakers 34,
who are not within immediate reach of the elderly person or
patient. The system is small, lightweight, ergonomic, unobtrusive,
and operates autonomously. In addition, the device is
self-contained and does not require wiring, installation,
calibrations or programming that may be inconvenient or overly
difficult for elderly persons to perform.
[0022] Referring now to FIG. 2, an important characteristic of the
present invention is that acceleration and body position sensors 20
in the monitor provide information for detecting or predicting a
fall. This is performed using a software method 22 or digital
signal processing (DSP) which recognizes body movement and position
patterns that indicate a fall has occurred or could occur. When
fall patterns are detected, a fall warning can be transmitted to a
careprovider via a communications link 24. It is also possible to
produce a preliminary fall warning to an elderly patient who is
wearing the device so they will be encouraged to lay or sit
down.
[0023] The monitor device communicates with a pager monitor or
station using conventional RF (radio frequencies) 28 or automated
telephone dialer technology. A signal transmitted to the pager
monitor or station is then transferred to an operator or attendant
who can dispatch emergency care or treatment. The monitor device
may also be connected to a cellular data modem 26 for transmitting
an alert signal to a commercial paging network. A wireless
communication link allows the patient to be monitored regardless of
their location. When the monitoring device is connected to a
cellular or another communications network, a patient can be
monitored while they are outside, traveling, or shopping.
[0024] This system uses an effective method for detecting the fall
of elderly people using accelerometers attached at the waist. The
inventors of this invention believe that the lack of the
significant prior work using accelerometers to detect falls is due
to the lack of an effective computational method. This device
includes at least two robust methods that can distinguish between
the falls and non-falls.
[0025] The fall monitoring hardware includes at least one
accelerometer unit which has one two-axis accelerometer. The two
units are packaged in a configuration with one axis from each unit
orthogonal to the other, while two other axes are arranged
anti-parallel and orthogonal to the first two. In this way, the
sensor system formed by the two accelerometers, measures three
orthogonal accelerations. When the device is attached to the waist
with a stiff belt (e.g., a leather belt) and the anti-parallel axes
are more or less vertical.
[0026] In a preferred embodiment, the accelerometers have a G
(gravity) range of +/-2 Gs and provide both a pulse width modulated
(PWM) output or a voltage output that has a sensitivity of 300
mV/G. The PWM output is preferably used. The PWM output of the
accelerometer is converted to gravity values by an onboard
microcontroller. An important function of the software connected to
the accelerometer is to set the sampling rate at a fixed interval.
This function is accomplished using a microcontroller counter for
precisely determining the time interval. In order to facilitate the
data processing, it is important that the sampling period of the
data acquisition be fixed. Such data acquisition is called true
real time data acquisition.
[0027] Now the biomechanics of the present method and device will
be discussed. The method must analyze the fall process, time
duration, and impact amplitude. These issues are determining
factors in distinguishing uncontrolled falls from controlled falls,
such as lying down to rest.
[0028] In the steady state after a fall event, the user's body is
defined to be horizontal. The accelerometer-based sensor is capable
of measuring the gravity vector in the sensor coordinate system. If
the sensor coordinate system moves with the user, the gravity
vector may be used to indicate when the user is in the horizontal
position or upright position.
[0029] Since the gravity vector is static in the global coordinate
system but changes orientation in our sensor coordinate system, two
cones 40, 42 are considered in our sensor coordinate system as
shown in FIG. 3. When the gravity vector is in the upper cone 40,
the user is in the upright position. This system must measure the
falling time and the length of time a person remains horizontal.
Both uncontrolled falls and controlled falls consist of the change
of the body orientation. Specifically, the body angle 44 changes
from approximately vertical to horizontal. In the preferred
embodiment of the invention, it is assumed that during the
uncontrolled fall, and after the fall, the body remains in a
horizontal orientation for at least two seconds. Such an assumption
does not assume that the body cannot roll along a horizontal
axis.
[0030] In the event of fall, the downward acceleration of the
center of the mass is approximately less than or equal to g
(gravity). The time it takes for the center of mass to reach the
ground is governed by the equation t={square root}{square root over
(d/g)}, where d is the distance of travel and t is the time for the
object to travel at the acceleration of g through the distance of
d. Assuming that the average height of a person is about 6 feet and
the center of mass in the vertical direction is 3.5 feet, the time
it takes for the center of mass to reach the ground is
approximately 0.335 seconds. The timing is important in determining
the filtering cutoff frequency.
[0031] Since the initial downward velocity in the event of a fall
is substantially zero, the velocity of the center of mass before
reaching the ground (before the impact) is v=g.multidot.t, where v
is the velocity before the impact and t is the time that the center
of mass traveled at the acceleration of g starting from zero
initial velocity. The velocity from the above equation determines
the minimum impact acceleration.
[0032] The present method assumes that there are two kinds of the
falls: controlled and uncontrolled. A controlled fall is defined as
a fall where the subject does not lose consciousness in the fall
process, and actively tries to prevent the fall from happening. An
uncontrolled fall is when the subject loses consciousness at some
time during the fall process or is unable to prevent the fall from
happening. Certain events, such as going to bed or sitting on a
sofa, are not defined as a fall. These events typically consist of
a two-event process (sit, then lie down).
[0033] The falling events are distinguished from the lying event by
the two-event process. The uncontrolled falling event is
distinguished from the controlled falling event by the time
duration. An uncontrolled event will have a shorter time period
than a controlled event.
[0034] Effectively, three components of acceleration are measured
with an intrinsic sensor coordinate system or Cartesian coordinate
system. The gravitational acceleration vector 46 is tracked in the
sensor coordinate system, as shown in FIG. 4, for determining if
the subject has fallen or not. It should be mentioned that the
measurement of a gravitational vector is corrupted by the subject's
body acceleration due to voluntary movements, but this can be taken
into account. If the gravitational acceleration vector is lined up
with the z-axis when the subject is standing up, the lying
condition can be conveniently determined by examining the angle
formed by the gravitational vector and the z-axis vector.
[0035] However, it is possible that the sensor unit might not be
mounted perfectly on the subject's waist. The gravitational vector
might not be lined up with the z-axis as shown in FIG. 3. In order
to increase the accuracy of the fall detection method, the sensor
coordinate system is rotated so that when the user's body is in the
upright position and the gravity vector is aligned with a new
z-axis. A coordinate rotation can be easily accomplished by
conducting a calibration procedure when the user first puts on the
sensor unit. The calibration creates another coordinate system
called the corrected coordinate system, which is also fixed to the
sensor system and rotated with respect to the intrinsic sensor
coordinate system. The gravity vector is now lined up with its
z-axis in the corrected coordinate system when the user is standing
up. The Y-axis of the corrected coordinate system is determined by
making it perpendicular to both the Z-axes of the intrinsic sensor
coordinate system and the gravity vector. A direction is determined
by using the right-hand rule, assuming the z-axis in the intrinsic
sensor coordinate is rotated to the gravity vector. The X-axis of
the corrected coordinate system is also determined by the
right-hand rule based on the Z and Y-axes of the corrected
coordinate system. The transformation of any vector from the
corrected coordinate system to the sensor coordinate system can be
established with two consecutive rotations: first, rotate about
Y.sub.c by q, then rotate about Z by a. The rotation matrices are
given below: 1 [ x y z ] = ( [ cos - sin 0 sin cos 0 0 0 1 ] [ cos
0 sin 0 1 0 - sin 0 cos ] ) - 1 [ x y z ]
[0036] The next expression is needed for computing the
gravitational vector in the corrected coordinate system from the
sensor intrinsic coordinate system in real time. 2 [ x y z ] = ( [
cos - sin 0 sin cos 0 0 0 1 ] [ cos 0 sin 0 1 0 - sin 0 cos ] ) - 1
[ x y z ]
[0037] Even though this step requires matrix multiplication and
inversion, it only needs to be done once and does not need to be
computed in real time.
[0038] The detailed steps required to detect the fall of an
individual wearing the present device will now be discussed. The
first step is detecting if the person's body has become
substantially horizontal. If the fall event (controlled or
uncontrolled) occurred, the person's body is in a steady state
indicative of a fall for at least two seconds. This steady state
will be substantially horizontal or greater than 45.degree. in most
cases. In this step, the angle .alpha. or the body angle data is
monitored for at least two seconds.
[0039] If the lying condition has been met, the second step is
examining the fall duration by looking at the data stored in a
buffer. If the fall duration is below a time threshold, it is an
uncontrolled fall. The fall duration is computed by examining the
time event of the angle .alpha. or the body angle data.
[0040] The next step is determining the severity of the fall. Once
an uncontrolled fall is detected, the rate of angular change of the
body angle and acceleration amplitude change is used for
determining the severity of the fall. A large rate of angular
change and a large acceleration amplitude indicate a severe fall.
The rate of angular body angle change and acceleration amplitude
are compared to a severity threshold to determine whether a severe
fall has taken place. The sensor system outputs four acceleration
measures. Two of them are the X and Y axis acceleration
measurements. The other two are Z-axis measurements. One
measurement is in positive Z direction and the other is negative
anti-parallel. After changing the sign of one of the negative
Z-axis measurements, then the average of the two z-axis
measurements is computed. The average is used as the final Z-axis
measurement.
[0041] The three axis acceleration measurements are filtered with a
5 Hz, second order Chebychev digital filter. This filtering can be
done with analog circuitry in order to reduce the computational
requirements. FIG. 5 depicts a comparison of the raw data and
filtered data. For example, the raw data for the first displayed
input line is ax and the filtered data for that input line is
axf.
[0042] In this detection method, at least two seconds of data are
collected. After performing the data preprocessing as described
above, the mean of the acceleration for each axis is computed. The
mean values of the three components of acceleration are used to
compute the a and q used in the transformation matrices. After the
calibration, the new acceleration is computed in the corrected
coordinate system.
[0043] The angle .alpha. (FIG. 3 and FIG. 4) between the vertical
axis and the body long axis plays an important role in fall
detection. By converting the Cartesian representation into polar
coordinates, the angle .alpha. is explicit. After the calibration
is done, the system works with the data in the polar coordinate
system.
[0044] After filtering, calibrating and representing the data in
the polar coordinate system, the system is ready to detect if the
user is in a substantially prone condition. Lying down (or a prone
position) is defined as the user being in a horizontal orientation
for at least two seconds. The system reads back through a 0.6
second window in time from the current sensor reading. If 75-80% of
the data points in this window are greater than the threshold value
(65.degree.), this means that the user is lying down.
[0045] After detecting that the user is lying down, the system back
traces through the fall data in the ring buffer to determine if a
fall has occurred. The fall detection device should contain at
least 4 seconds of buffered data or up to 10 seconds of data. The
fall detection system assumes that an uncontrolled fall is a faster
event than a controlled fall. The maximum and minimum angles are
computed in a 0.2 second buffer window. If the difference between
maximum and minimum acceleration exceeds a maximum body angle
threshold, this means that an uncontrolled fall has occurred.
Another valuable maximum body angle threshold is 50.degree.,
because if an individual body angle exceeds this angle .alpha. fall
is likely to have occurred.
[0046] Notice the large ".alpha." angle change 50 due to the fall
as depicted in FIG. 7. Each line in the figure represents a body
position with respect to the vertical. The angle .alpha. represents
body position, and length of the line represents magnitude of the
acceleration vector.
[0047] FIG. 8 illustrates a flow chart of the method for detecting
a fall. The first step includes the calibration of a gravity vector
and a Cartesian coordinate transformation 60. Next the device
begins to sample data and measure the ".alpha." angle 62. The
measurements are stored in a buffer which is configured to hold a
certain number of data points (N). A predefined threshold is used
to decide if enough data points are past a certain threshold angle.
For example, the system will check to see if more than 75-80% of
the data points are greater than a 50.degree. angle from the
calibrated gravity vector. Of course, the method can also use a
threshold of 50% or more when checking the number of data points
past a critical angle. Once it has been determined that there are a
certain number of data points past a critical angle threshold and a
fall is occurring, the system continues reading and storing angle
data for a certain sampling time period T 64. The time T will
preferably be a period of two seconds or more.
[0048] After the sampling time period has ended, the system traces
backward in the buffer to obtain the starting point of the fall
event 66. The rate of the fall and the peak duration are used to
determine the starting point of the event. Then the event duration,
event amplitude, and rate amplitude are used to determine if there
is a fall and how severe the fall is 70. If the person is lying
down but the data does not indicate a fall occurred, then the data
window with N points is checked to determine if 75-80% of the data
points are less than a critical value 68. If the criteria are met
(no fall has occurred), the system returns to its original fall
sampling mode. Otherwise, the system waits until these criteria are
met before it returns to fall testing. This last step also allows
any trailing fall data to be flushed from the buffer before fall
testing begins again.
[0049] The overall accuracy of this method is almost 95% fall
detection when using data obtained from waist mounted
accelerometers. The fall detection system is also programmed to
send a data signal to a receiver that interfaces to a telephone
dialer.
[0050] FIG. 9 illustrates an alternative embodiment of steps used
to determine a fall. A simplification of the method discussed above
avoids using a coordinate transformation and only uses the change
in the Z axis acceleration. This avoids the complex coordinate
transformation and allows the use of a less powerful and more
affordable microcontroller with a short range radio link.
[0051] First a datastream is received from the Z-axis
accelerometers 100 and this information is stored in a 4-second
sample ring buffer 102. Of course, the buffer could be larger if
desired. The candidate event detector decides if an event is taking
place which could be the beginning of a fall. The event detector
takes the difference in acceleration between the current sample and
the previous sample 106. If the difference in acceleration between
the two samples exceeds a maximum parameter (MaxParam) 108 or is
less than a minimum parameter (MinParam) 110, then a potential fall
event may be starting. When the sample differences do not exceed a
certain maximum or minimum threshold, they are ignored.
[0052] If a fall has started, the steps in the fall discrimination
portion of the algorithm are performed. Since a fall can be
occurring, 2 more seconds of samples are collected 112. Next, the
largest samples above and below zero gravity (zero-g) are found in
the buffer 114. Then the absolute value of the difference of the
mean of the first and second halves of the buffer are computed. The
mean of the first half of the buffer is also stored for comparison
purposes.
[0053] The fall discriminator then tests whether the fall includes
a large vertical displacement. This is determined by comparing the
largest sample above zero-g with a maximum acceleration threshold
116 or by comparing the smallest sample below zero-g with a minimum
acceleration threshold 118. If either of these criteria is met,
then a fall signal is produced. A vertical fall is also tested by
comparing the absolute value of the mean of the first and second
halves of the buffer with a maximum mean threshold 120. This
measurement detects a large fall with a sustained acceleration that
does not exceed a maximum or minimum threshold.
[0054] The fall discriminator also decides whether a fall has taken
place with only a little vertical displacement. This is determined
by comparing the largest sample above zero-g with 1.5 times the
maximum acceleration threshold or by comparing the smallest sample
below zero-g with 1.5 times the minimum acceleration threshold. If
either of these criteria are met, then a fall signal is produced. A
fall with little vertical displacement, such as rolling off a bed,
is also tested by comparing the mean of the first half of the
buffer with a predefined threshold or "flat" threshold. This
measurement detects a rolling fall with sustained acceleration.
[0055] Although this invention does not directly prevent falls, the
monitoring device does provide a number of advantages. For example,
it enables the rapid arrival of help which decreases further damage
caused by delayed intervention and medical care. The patient also
receives a better prognosis for both short term medical aid and
long term recovery due to the faster arrival of help. A historical
recording of the fall event aids in determining the problem
severity and what action should be taken by caregivers. These
advantages lead in turn to the patient having greater confidence to
live independently. Increased safety at a low cost is also provided
because a patient can live without continuous companionship and/or
assistance.
[0056] In nursing homes where a monitoring station is installed,
the system facilitates diagnosis by providing a history of a
patient's body motion and position directly preceding, during, and
after a fall. The result is improved patient care in nursing home
facilities with smaller numbers of nursing personnel. In the case
of elderly patients living independently in homes, they receive
increased safety and extended length of independent quality
life.
[0057] Other advantages are provided for nursing home personnel.
The nursing home can monitor the actual physical status of all
their patients without individual observation. Then if a fall or
some similar accident occurs, the nursing home will immediately
know the severity of the fall and the current body position of the
patient. Information about the fall and the body position of the
patient during and after the fall is recorded to provide a history
for the patient.
[0058] There are also advantages produced for the patient's
relatives and other part time careproviders. Relatives who are
caring for the patient or elderly person who has the present device
will be able to work outside the home without the need for hiring
permanent, live-in help. The automatic sensors and monitoring of
this invention also allow relatives and careproviders to have
increased confidence that in case of an emergency the fall monitor
will provide instant notification. Professional careproviders also
benefit from the fall monitor because there in an improved ability
to organize help quickly with a minimum of interruption to their
other professional activities. This is because the device can
detect the severity of the accident and then the appropriate
emergency care may be sent without the careprovider providing
further analysis.
[0059] It is to be understood that the above-described arrangements
are only illustrative of the application of the principles of the
present invention. Numerous modifications and alternative
arrangements may be devised by those skilled in the art without
departing from the spirit and scope of the present invention and
the appended claims are intended to cover such modifications and
arrangements. Thus, while the present invention has been shown in
the drawings and fully described above with particularity and
detail in connection with what is presently deemed to be the most
practical and preferred embodiment(s) of the invention, it will be
apparent to those of ordinary skill in the art that numerous
modifications, including, but not limited to, variations in size,
materials, shape, form, function and manner of operation, assembly
and use may be made, without departing from the principles and
concepts of the invention as set forth in the claims.
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