U.S. patent application number 13/377419 was filed with the patent office on 2012-04-19 for fall prevention.
This patent application is currently assigned to KONINKLIJKE PHILIPS ELECTRONICS N.V.. Invention is credited to Warner Rudolph Theophile Ten Kate.
Application Number | 20120095722 13/377419 |
Document ID | / |
Family ID | 42830315 |
Filed Date | 2012-04-19 |
United States Patent
Application |
20120095722 |
Kind Code |
A1 |
Ten Kate; Warner Rudolph
Theophile |
April 19, 2012 |
FALL PREVENTION
Abstract
There is provided a method of determining a fall risk of a user,
the method comprising collecting measurements of the motion of the
user, estimating a value for a parameter related to the gait of the
user from the measurements, and determining a fall risk for the
user from a comparison of the estimated value with a normal value
for the parameter determined from motion of the user in which the
user is at their normal risk of falling.
Inventors: |
Ten Kate; Warner Rudolph
Theophile; (Eindhoven, NL) |
Assignee: |
KONINKLIJKE PHILIPS ELECTRONICS
N.V.
EINDHOVEN
NL
|
Family ID: |
42830315 |
Appl. No.: |
13/377419 |
Filed: |
July 6, 2010 |
PCT Filed: |
July 6, 2010 |
PCT NO: |
PCT/IB10/53090 |
371 Date: |
December 9, 2011 |
Current U.S.
Class: |
702/141 |
Current CPC
Class: |
A61B 5/1117 20130101;
A61B 5/7264 20130101; A61B 5/7282 20130101; G16H 50/20 20180101;
G08B 21/0446 20130101; G01C 22/006 20130101; G01P 15/00
20130101 |
Class at
Publication: |
702/141 |
International
Class: |
G06F 15/00 20060101
G06F015/00; G01P 15/00 20060101 G01P015/00 |
Foreign Application Data
Date |
Code |
Application Number |
Jul 10, 2009 |
EP |
09165127.3 |
Claims
1. A method of determining a fall risk of a user, the method
comprising: collecting measurements of the motion of the user;
estimating a value for a parameter related to the gait of the user
from the measurements; and determining a fall risk for the user
from a comparison of the estimated value with a normal value for
the parameter determined from motion of the user in which the user
is at their normal risk of falling.
2. A method as claimed in claim 1, wherein the step of determining
a fall risk comprises weighting the comparison between the
estimated value and the normal value according to a standard
deviation of the normal value.
3. A method as claimed in claim 1, wherein the estimated value is
determined from motion of the user over a period of time that is
shorter than the period of time over which the normal value is
determined.
4. A method as claimed in claim 1, wherein the step of estimating
comprises identifying a step boundary in the collected
measurements.
5. A method as claimed in claim 4, wherein the step of identifying
a step boundary comprises identifying clusters of contiguous
measurements in the collected measurements in which the magnitude
of each of the measurements exceeds a threshold.
6. A method as claimed in claim 4, wherein the step of identifying
a step boundary comprises identifying clusters of contiguous
measurements in the collected measurements in which the magnitude
of each of the measurements exceeds a threshold, apart from a
subset of the measurements whose magnitude is less than the
threshold, provided that the subset covers a time period less than
a time threshold.
7. A method as claimed in claim 4, wherein the step of identifying
a step boundary comprises identifying clusters of contiguous
measurements in the collected measurements, wherein the first
collected measurement in the collected measurements whose magnitude
exceeds a first threshold denotes the first measurement in a
cluster and wherein the first collected measurement after the first
measurement in the cluster whose magnitude falls below a second
threshold denotes the last measurement in the cluster, provided
that the last measurement is more than a minimum period after the
first measurement.
8. A method as claimed in claim 5, wherein the step of identifying
step boundaries further comprises identifying the step boundary as
the measurement in each cluster with the highest magnitude.
9. A method as claimed in claim 4, wherein the parameter related to
the gait of the user comprises a step size and the step of
estimating a value for the parameter comprises integrating
horizontal components of the collected measurements with the
integral bounds being given by consecutive identified step
boundaries.
10. A method as claimed in claim 9, wherein the step of estimating
a value for the parameter comprises computing a double integration
with respect to time of the horizontal components of the collected
measurements relating to acceleration, the integration constants
being set to zero at the beginning of the step.
11. A method as claimed in claim 4, wherein the parameter related
to the gait of the user comprises, or additionally comprises, a
forward step size and the step of estimating a value for the
parameter comprises: integrating horizontal components of the
collected measurements with the integral bounds being given by
consecutive identified step boundaries to give a start and end
position for a step; and determining the forward step size as the
norm of the vector connecting the start and end positions.
12. A method as claimed in claim 10, wherein the parameter related
to the gait of the user additionally comprises a lateral step size
and the step of estimating a value for the parameter further
comprises: defining a straight line between the start and end
positions; integrating collected measurements occurring during the
step to give a series of positions during the step; determining the
distance between each position and the straight line; and
determining the lateral step size as the maximum distance in this
series.
13. A method as claimed in claim 1, further comprising a
calibration step that includes: collecting measurements of the
motion of the user when the user is at their normal risk of
falling; and estimating the normal value for the parameter related
to the gait of the user from the collected measurements.
14. A fall prevention device, comprising: at least one sensor for
collecting measurements of the motion of a user of the device; and
a processor for estimating a value for a parameter related to the
gait of the user from the measurements, and for determining a fall
risk for the user from a comparison of the estimated value with a
value of the parameter determined from motion of the user in which
the user is at their normal risk of falling.
15. A computer program product comprising computer-readable code
that, when executed on a suitable computer or processor, is
configured to cause the computer or processor to perform the steps
in the method defined in claim 1.
Description
TECHNICAL FIELD OF THE INVENTION
[0001] The invention relates to a method and device for monitoring
the motion of a user, and in particular to a method and device for
determining a fall risk for a user.
BACKGROUND TO THE INVENTION
[0002] Falls affect millions of people each year and result in
significant injuries, particularly in the elderly. In fact, it has
been estimated that falls are one of the top three causes of death
in elderly people.
[0003] A fall is defined as a sudden, uncontrolled and
unintentional downward displacement of the body to the ground.
There are currently some fall detection systems available that
detect these falls and allow the user to obtain assistance manually
or automatically if a fall occurs. Exemplary fall detectors can
comprise personal help buttons (PHBs) or worn and/or
environment-based automatic detectors.
[0004] Automatic fall detectors comprise one or a set of sensors
that measure the movement of the user, and a processor that
compares the measured or processed signals with predetermined
thresholds in order to detect a fall. In particular, automatic fall
detectors store a set of predetermined threshold values and/or
parameter sets. When the detector is activated, movement data
obtained from the sensors (such as, for example, an accelerometer)
will be continuously transformed and processed, and then compared
with those parameter sets to determine if a fall event occurs.
[0005] Although these fall detectors are useful, they do not
actually prevent falling, and only provide a warning or alarm in
the event that a user already has fallen.
[0006] However, users that are insecure during walking, for example
caused or enhanced by a fear of falling, by fatigue in the muscles,
by frequently multi-tasking (i.e. they are carrying items when
walking, talking to their grandchild, etc, or that move in places
where there is dim lighting, a wet or irregular ground
surface--such as loose carpet, electricity wires, toys, tools, and
other hazards) or that are under medication that may affect balance
or concentration, can be assisted by a device for fall prevention
that decreases the actual risk of falling, or at least alerts the
user that they are at a higher risk of falling at a particular
time, and makes them feel more safe.
[0007] There is therefore a need for a method and device that can
determine an instantaneous fall risk for a user.
SUMMARY OF THE INVENTION
[0008] According to a first aspect of the invention, there is
provided a method of determining a fall risk of a user, the method
comprising collecting measurements of the motion of the user;
estimating a value for a parameter related to the gait of the user
from the measurements; and determining a fall risk for the user
from a comparison of the estimated value with a normal value for
the parameter determined from motion of the user in which the user
is at their normal risk of falling.
[0009] In a preferred embodiment, the step of determining a fall
risk comprises weighting the comparison between the estimated value
and the normal value according to a standard deviation of the
normal value.
[0010] Preferably, the estimated value is determined from motion of
the user over a period of time that is shorter than the period of
time over which the normal value is determined.
[0011] In a preferred embodiment, the step of estimating comprises
identifying a step boundary in the collected measurements.
[0012] In one embodiment, the step of identifying a step boundary
comprises identifying clusters of contiguous measurements in the
collected measurements in which the magnitude of each of the
measurements exceeds a threshold.
[0013] In an alternative embodiment, the step of identifying a step
boundary comprises identifying clusters of contiguous measurements
in the collected measurements in which the magnitude of each of the
measurements exceeds a threshold, apart from a subset of the
measurements whose magnitude is less than the threshold, provided
that the subset covers a time period less than a time
threshold.
[0014] In another alternative embodiment, the step of identifying a
step boundary comprises identifying clusters of contiguous
measurements in the collected measurements, wherein the first
collected measurement in the collected measurements whose magnitude
exceeds a first threshold denotes the first measurement in a
cluster and wherein the first collected measurement after the first
measurement in the cluster whose magnitude falls below a second
threshold denotes the last measurement in the cluster, provided
that the last measurement is more than a minimum period after the
first measurement.
[0015] In any of these alternative embodiments, the step of
identifying step boundaries can further comprise identifying the
step boundary as the measurement in each cluster with the highest
magnitude.
[0016] In a preferred embodiment, the parameter related to the gait
of the user comprises a step size and the step of estimating a
value for the parameter comprises integrating horizontal components
of the collected measurements with the integral bounds being given
by consecutive identified step boundaries.
[0017] In a preferred embodiment, the step of estimating a value
for the parameter comprises omitting the average velocity of the
user from the integration such that the step size is determined
based on the variation of the velocity.
[0018] In one embodiment, the parameter related to the gait of the
user comprises, or additionally comprises, a forward step size and
the step of estimating a value for the parameter comprises
integrating horizontal components of the collected measurements
with the integral bounds being given by consecutive identified step
boundaries to give a start and end position for a step; and
determining the forward step size as the norm of the vector
connecting the start and end positions.
[0019] Preferably, the parameter related to the gait of the user
additionally comprises a lateral step size and the step of
estimating a value for the parameter further comprises defining a
straight line between the start and end positions; integrating
collected measurements occurring during the step to give a series
of positions during the step; determining the distance between each
position and the straight line; and determining the lateral step
size as the maximum distance in this series.
[0020] In preferred embodiments, the method further comprises a
calibration step that includes collecting measurements of the
motion of the user when the user is at their normal risk of
falling; and estimating the normal value for the parameter related
to the gait of the user from the collected measurements.
[0021] Preferably, the step of estimating comprises estimating
values for a plurality of parameters related to the gait of the
user from the measurements, and the step of determining a fall risk
comprises comparing the estimated values with values of the
parameters determined from motion of the user in which the user is
at a low risk of falling.
[0022] Preferably, the parameter or parameters related to the gait
of the user is/are selected from step size, step width, step time,
double support time, gait velocity, cadence, average step size,
average step time, average double support time, average gait
velocity and average cadence.
[0023] In accordance with a second aspect of the invention, there
is provided a fall prevention device, comprising at least one
sensor for collecting measurements of the motion of a user of the
device; and a processor for estimating a value for a parameter
related to the gait of the user from the measurements, and for
determining a fall risk for the user from a comparison of the
estimated value with a value of the parameter determined from
motion of the user in which the user is at their normal risk of
falling.
[0024] In accordance with a third aspect of the invention, there is
provided a computer program product comprising computer-readable
code that, when executed on a suitable computer or processor, is
configured to cause the computer or processor to perform the steps
in the method described above.
[0025] In accordance with alternative aspects of the invention,
there are provided methods for determining gait parameters,
including step boundaries, a step size, a forward step size and/or
a lateral step size as described above and in the following
Detailed Description.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Embodiments of the invention will be described, by way of
example only, with reference to the following drawings, in
which:
[0027] FIG. 1 shows a fall prevention device according to the
invention being worn by a user;
[0028] FIG. 2 shows the fall prevention device of FIG. 1 in more
detail;
[0029] FIG. 3 is a flow chart illustrating the steps in a method
according to the invention;
[0030] FIG. 4 is a plot illustrating measurements from an
accelerometer that is in a pendant worn around a user's neck;
[0031] FIG. 5 is a plot illustrating measurements from an
accelerometer that is worn on a user's ankle; and
[0032] FIG. 6 is a plot illustrating the derivative of measurements
from an accelerometer that is worn on a user's ankle.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0033] Although the invention will be described in terms of a
method and device that is used for fall prevention, it will be
appreciated that the method and device can be provided with
additional functionality so that the device can also be used as a
fall detector.
[0034] In a preferred embodiment of the invention, the fall
prevention device comprises a single unit that is worn by a
user.
[0035] FIG. 1 shows a fall prevention device 2 in the form of a
pendant being worn around the neck of a user 4 and FIG. 2 shows the
fall prevention device 2 in more detail.
[0036] The fall prevention device 2 comprises three sensors, an
accelerometer 6, a magnetometer 8 and a gyroscope 10, which are
connected to a processor 12. The processor 12 receives measurements
from the sensors 6, 8, 10 and processes the measurements to
determine if the user 4 of the fall prevention device 2 is at a
higher risk of falling.
[0037] In particular, the processor 12 processes the measurements
from the accelerometer 6, magnetometer 8 and gyroscope 10 to
determine the orientation of the fall prevention device 2, and
processes the measurements from the accelerometer 6 (using the
determined orientation) to determine parameters relating to the
motion (and specifically the gait) of the user 2.
[0038] It should be understood that the three sensors are included
by way of example only. Using sensor fusion, the measurements of
the three sensors can complement each other for an optimal estimate
of the orientation, as is known to those skilled in the art. Other
sensors, for example a barometer and GPS receiver, can be added to
further improve the accuracy of the parameters estimated by the
device 2. It will be further appreciated that sensors can be
omitted to reduce the power consumption and cost of the device
2--for example in a minimal device 2, only the accelerometer 6 is
present, in which case it is only possible to estimate the
inclination of the device 2.
[0039] The fall prevention device 2 also comprises an alarm 14 that
can be activated by the processor 12 to warn or notify the user 4
that they are at an increased risk of falling. The alarm 14 can
also comprise (or be replaced by) some means that provides the user
4 with an indication of their current risk of falling, even if they
are not currently at a high risk of falling. For example, the fall
prevention device 2 could be provided with means for providing
vibrotactile or auditory feedback, or with a series of lights (or a
light that can show different colors) which can be illuminated to
indicate the current fall risk (for example there could be lights
associated with the user 4 being at low, medium and high risk of
falling).
[0040] The fall prevention device 2 also comprises transmitter
circuitry 16 that allows the fall prevention device 2 to transmit
an alarm or warning signal to a base station associated with the
fall prevention device 2 (which can then issue an alarm or summon
help from a healthcare provider or the emergency services). The
base station can also carry out further processing of the sensor
measurements, and/or store the sensor measurements for later
analysis. In alternative embodiments, the transmitter circuitry 16
may be omitted if the fall prevention device 2 does not need to
contact a base station to issue an alarm or summon help from a
healthcare provider (for example if the fall prevention device 2
can contact the healthcare provider by using sound).
[0041] In some embodiments of the invention (not represented by the
device 2 shown in FIG. 2), the fall prevention device 2 can include
a memory unit for storing the sensor measurements for later
analysis.
[0042] It is known that a large variance in gait parameters (i.e.
parameters associated with the gait of a user) corresponds to a
high risk of falling. However, this is a static relationship; the
variance is an average obtained during general walking, and is
related to an average risk of falling, which does not necessarily
provide any information about the current risk of falling for the
user 4. Gait parameters can include measures such as step size,
step width, step time, double support time (i.e. the time that both
feet are in contact with the ground), gait velocity, and cadence.
The gait parameters can also include averages of the above measures
over a few steps, so for example an average step size, average step
width, average step time, average double support time, average gait
velocity and average cadence.
[0043] In accordance with the invention, the fall prevention device
2 determines values for gait parameters from sensor measurements
covering a short period of time and compares these values with
normal values for the user 4 (i.e. values that are obtained when
the user 4 is at their normal (i.e. preferably a low or minimal)
risk of falling). In particular, the fall prevention device 2 can
determine various gait parameters from sensor measurements
covering, say, 12 steps (so 6 strides). The normal values for the
user 4 can be obtained by collecting measurements while the user 4
is walking steadily for a period of time (for example a minute) or
for a certain number of steps (for example 40 steps).
[0044] A method of determining a dynamic risk of falling in
accordance with the invention is shown in FIG. 3. In step 101, the
sensors 6, 8, 10 take measurements of the motion of the user 4 and
in step 103, the processor 12 estimates values for the required
gait parameters from the measurements. In step 105, the processor
12 then compares these estimates to usual values of these gait
parameters for the user 4 to determine the user's dynamic risk of
falling. As indicated above, the usual values correspond to those
observed when the user 4 is at their normal (i.e. preferably a
minimal or low) risk of falling.
[0045] The normal or usual values for the gait parameters can be
obtained during a calibration session before the fall prevention
device 2 is used (for example the user 4 can wear the fall
prevention device 2 while it is in a calibration mode, and the fall
prevention device 2 can determine values for each gait parameter
while the user 4 is walking normally).
[0046] In a preferred embodiment of the invention, the comparison
between the estimated values and the normal values is weighted
according to the standard deviation of the normal gait parameter
values.
[0047] For example, if .mu. epresents the calibration mean (i.e.
the mean of the normal values for a particular parameter), .sigma.
represents the standard deviation in that calibration mean, and a
represents the currently observed parameter value, a deviation is
signaled if
a - .mu. .sigma. ( 1 ) ##EQU00001##
exceeds a threshold.
[0048] For example,
exp [ - ( a - .mu. ) 2 2 .sigma. 2 ] ( 2 ) ##EQU00002##
maps to a value between 0 and 1, where 1 indicates normal gait (for
that user), and a deviation from normal gait is signaled (i.e. the
user is at a higher risk of falling) if the result falls below a
threshold, for example 0.7.
[0049] It will be appreciated that the values for .mu. and .sigma.
are usually dependent on the user and will need to be set
independently for each user.
[0050] In one embodiment of the invention, the processor 12
estimates values for an average step size and an average step
width. However, in other embodiments of the invention, other
combinations of gait parameters can be used.
[0051] The operation of the processor 12 in determining estimates
for specific gait parameters will be described further below. As it
will be shown, in the embodiment in which the fall prevention
device 2 is a pendant located around the neck of the user 4, the
way in which the averages a and .mu. are calculated relates them to
the variance in step size rather than to the mean, as will be
explained further below. Thus, the invention effectively compares
current and normal variances. It should be noted that a is
typically averaged over a shorter period than .mu..
[0052] In addition, although in the preferred embodiment of the
invention the fall prevention device 2 is in the form of a pendant
to be worn around the neck of a user 4, it will be appreciated that
the invention can be implemented in alternative forms that are to
be worn on different parts of the body of the user 4, such as at
the waist or on the ankle of the user 4. As described further
below, in these embodiments, it is necessary to modify the
processing used to determine the gait parameters from the sensor
measurements.
[0053] Moreover, depending on the particular gait parameters that
the processor 12 determines from the sensor measurements (and if
the movements of the user 4 do not cause fast rotations of the fall
prevention device 2), the gyroscope 10, the magnetometer 8 or both
the magnetometer 8 and gyroscope 10 can be omitted from the device
2. In the embodiment in which the fall prevention device 2 is a
pendant, it has been found that the gait variation estimate is less
sensitive without the gyroscope 10, but it is still capable of
detecting deviations from the normal gait.
Estimating the Step Size
[0054] In order to obtain an estimate of the step size, a number of
processing steps are required. In particular, it is necessary to
estimate the step boundaries and the orientation of the
accelerometer (so that the accelerometer measurements can be
transformed to Earth coordinates) in order to estimate the step or
stride size.
[0055] Estimating step boundaries--It is important to accurately
estimate step boundaries (which are defined as the moment of heel
strike (HS), i.e. when the swing leg is touching the ground again,
starting the stance phase), since missing a boundary will cause a
significant deviation in a, hence leading to an alarm.
[0056] Step boundaries can also be used to estimate stepping time,
which is another gait parameter, and, when combined with step size,
allows an estimate of walking velocity to be determined.
[0057] For accelerometers that are rigidly attached to the upper
body of a user, step boundaries are usually found by observing the
`zero` crossing of the vertical acceleration. Of course, it will be
appreciated that the actual "crossing" will be through 1 g (9.81
ms.sup.-2) since gravity is always acting on the user. The vertical
acceleration will be known after the orientation of the
accelerometer is determined--although for rigidly mounted
accelerometers, the accelerometer reading along the coordinate axis
corresponding to vertical usually provides a sufficient
approximation.
[0058] However, in the preferred embodiment of the invention in
which the fall prevention device 2 is in the form of a pendant, the
fall prevention device 2 (and therefore the accelerometer 6) is
free to move relative to the user 4, which means that the
accelerometer coordinate system also moves relative to the user
4.
[0059] Therefore, it is preferred to modify the detection of step
boundaries (i.e. heel strikes) as follows. Firstly, the norm of the
signal from the accelerometer 6 is calculated. Then, the peaks in
this signal serve as boundary markers for each step, which closely
link to the heel strike (HS), as shown in FIG. 4.
[0060] The peaks are found using a two-step procedure. First,
so-called clusters are identified. Second, the maximum value in
each cluster is identified as the step boundary. The clusters are
found as the range of samples that are above a certain threshold
(typically 2 ms.sup.-2 above gravity, i.e. .about.12 ms.sup.-2),
where a small gap of samples not surpassing that threshold is
permitted (typically 0.3 times a typical step time (which is around
0.5 seconds), i.e. 0.15 sec).
[0061] This algorithm works for accelerometers 6 that are attached
to the upper body of the user 4. If the accelerometer 6 is attached
at or to the lower body of the user 4 (for example the ankle), then
usually two clusters appear per step, or actually per stride, as
shown in FIG. 5. Since the accelerometer signal is observed at the
ankle, the period of a stride (i.e. a step with both the left foot
and the right foot) is seen.
[0062] One cluster corresponds to the lifting of the foot, and the
other corresponds to the heel strike. It can be difficult to decide
which of the two is the heel strike. However, it can also be
observed that a single minimum occurs per step, and this can be
used instead for detecting the step boundaries.
[0063] FIG. 5 also shows another way to identify the clusters.
Instead of a single threshold (with a small gap being permitted),
two thresholds are used. They introduce a hysteresis--surpassing
one threshold indicates the start of a cluster, and falling below
the second threshold indicates the end of a cluster, provided that
the measurements fall below the second threshold after a minimum
duration from the first measurement in the cluster. Preferably, the
minimum duration is derived from the step time, as described
above.
[0064] The unique minimum shown for each stride in FIG. 5
corresponds to the swing phase and it is less favorable to use this
as the step boundary. Instead, it is preferable to use the heel
strike as the step boundary for several reasons. Firstly, this is a
clearly defined event. Secondly, when measuring at the feet or
ankle, at heel strike the velocity relative to the ground is zero,
which can be used in estimating the movement. Thirdly, at heel
strike, the acceleration in the horizontal direction is low, which
leads to lower errors when (double) integrating the acceleration
for estimating the step size. The sample values at the beginning of
the integration have a large influence on the outcome, so large
values can cause a bias in the total outcome.
[0065] A better algorithm for detecting step boundaries when the
accelerometer 6 is located on the lower body of the user 4 is found
by observing the derivative of the acceleration, as shown in FIG.
6. The boundaries are more clear, and they associate with the heel
strike event.
[0066] It will be appreciated that as different algorithms can be
used for different fall prevention device 2 locations on the user's
body, the processor 12 in the fall prevention device 2 needs to
know the location used. The user 4 may be able to select an
appropriate location from a list of locations offered by the
processor 12, or, alternatively, the processor 12 can execute a
classifier algorithm to detect the location (and therefore the
algorithm to use to detect the step boundary) based on the
particular patterns in the measurements of the accelerometer 6.
[0067] Estimating accelerometer orientation--The orientation of the
accelerometer 6 (and therefore the fall prevention device 2) can be
estimated from the direction that gravity appears in the
accelerometer coordinate system. By defining the z-axis to
correspond with the vertical direction when the accelerometer 6 is
not tilted, the orientation follows through the vector dot product
of the measured gravity direction and the z-axis, i.e. through the
z-component of the measured gravity (as is known, when using
normalized values, the dot product equals the cosine of the
enclosed angle).
[0068] Since the accelerometer 6 is sensitive to both acceleration
due to gravity and acceleration due to movement, a filter is needed
in order to estimate which component is due to gravity. Usually, a
gyroscope 10 is added to measure the angular rotation speed and to
correct the measured acceleration correspondingly. However, if the
movements of the user 4 do not cause fast rotations of the fall
prevention device 2, the gyroscope 10 can be omitted.
[0069] Without fast rotations, the gravity component can be found
as the low-pass filtered component of the signal from the
accelerometer 6. Since causality constraints in the filter design
introduce a delay, the filtered acceleration signal needs to be
corrected for this delay.
[0070] From the measured direction of gravity, the inclination or
orientation of the fall prevention device 2 can be estimated.
However, this inclination or orientation does not provide
information about the horizontal direction of the device 2 (i.e.
which way the device 2 is facing), so the magnetometer 8 can be
used to determine the horizontal orientation of the device 2.
Gyroscopes can also be used in the estimation of horizontal
orientation.
[0071] Orientation can be expressed in different ways, of which
Euler angles and Euler parameters are the most commonly used. They
are usually implemented through matrices or quaternions. The
algebras are isomorphic, and provide a way to transform the
measured values (acceleration etc.) as expressed in the
accelerometer's local coordinate system to the global (Earth)
coordinate system.
[0072] Estimating step or stride sizes--Once the acceleration
signals have been transformed to the Earth coordinate system, the
step sizes can be computed by double integrating (with respect to
time) the horizontal components of these transformed signals. The
boundaries for the integrations are given by the estimated step
boundaries. The integration constants, velocity and position at the
beginning of the step, are both set to zero.
[0073] For the position this is fine, since the step size is
needed, which is the difference between end and start position.
[0074] For the velocity this is correct in the case that the device
2 is located on a foot of the user 4, since upon heel strike the
velocity relative to the Earth is zero. However, when the device 2
is located on the upper body of the user 4, for example when the
device 2 is a pendant, there will be a near constant velocity.
Therefore, setting the velocity to zero at each step boundary will
introduce errors, since a constant velocity yields a larger step
size estimate. However, if it is assumed that the velocity is
constant during the period of observation, i.e. during the
averaging period (typically around 10-12 steps for determining a,
and around 30-60 seconds for determining g), the constant velocity
component can be omitted. This means that the step size can be
computed based on the variation in the velocity, which means that
the device 2 (and the estimated fall risk) will become independent
of the current walking speed--only the deviations will be
observed.
[0075] The step size p follows as the integral of velocity v over
step time:
p=.intg..sub.HS.sub.0.sup.HS.sup.1vdt=
v(HS.sub.1-HS.sub.0)+.intg..sub.HS.sub.0.sup.HS.sup.1.DELTA.vdt
(3)
where HS.sub.0 and HS.sub.1 are the times of subsequent heel
strikes (HS) and v and .DELTA.v denote the average velocity and its
deviation respectively.
[0076] Defining the step time T=HS.sub.1-HS.sub.0= T+.DELTA.T, and
p= p+.DELTA.p, where p= v T, the deviation in step size follows
as:
.DELTA.p= v.DELTA.T+.intg..sub.HS.sub.0.sup.HS.sup.1.DELTA.vdt
(4)
[0077] It should be noted that, by definition, the mean of
(.DELTA.p).sup.2 equals the variance of p. The velocity deviation
.DELTA.v can be computed from the measured acceleration
through:
.DELTA.v(t)=.DELTA.v(HS.sub.0)+.intg..sub.HS.sub.0.sup.tadt'
(5)
At constant walking velocity,
.intg..sub.HS.sub.0.sup.HS.sup.1adt.apprxeq.0 (6)
and it is reasonable to assume that .DELTA.v(HS.sub.0) is roughly
the same at each step. Then, by neglecting the first term in
equation (4) above, .DELTA.p is approximated as:
.DELTA.p.apprxeq..intg..sub.HS.sub.0.sup.HS.sup.1.DELTA.v(HS.sub.0)dt+.i-
ntg..sub.HS.sub.0.sup.HS.sup.1dt.intg..sub.HS.sub.0.sup.tadt'=.DELTA.v(HS.-
sub.0)(HS.sub.1-HS.sub.0)+.intg..sub.HS.sub.0.sup.HS.sup.1dt.intg..sub.HS.-
sub.0.sup.tadt'=.mu.+.DELTA.s (7)
[0078] The second term, .DELTA.s, is the result of the double
integration of the acceleration when using zero integration
constants. Since, by definition, E[.DELTA.p]=0, it follows that
.mu.=-E[.DELTA.s] (8)
[0079] So, a first order approximation of g can be obtained by
observing the (horizontal) acceleration under stable walking
conditions and computing the mean of the double integrated
acceleration. This process also yields a standard deviation:
.mu..sub.0=mean(.DELTA.s.sub.stable) (9)
.sigma..sub.0=std(.DELTA.s.sub.stable) (10)
So, .mu..sub.0 estimates -.mu., and the variance of p follows
as
var(p)=E.left brkt-bot.(.DELTA.p).sup.2.right
brkt-bot.=.mu..sup.0+.mu..sub.0.sup.2+.sigma..sub.0.sup.2 (11)
[0080] In typical cases,
.sigma..sub.0.sup.2<<.mu..sub.0.sup.2 and
var(p).apprxeq.2.mu..sup.2. During operation, a running estimate
a=E[.DELTA.s] is made over a few steps, and is compared with
.mu..sub.0 relative to .sigma..sub.0, see equation (1). So,
although mean values are compared, they basically reflect variance
in the step sizes.
[0081] The values .mu..sub.0 and .sigma..sub.0 are user dependent
and need to be set for each user in a calibration phase. Note,
however, that since the average velocity v is not accounted for in
the calculations, the method is insensitive to the actual walking
speed of the user 4.
[0082] The first term v.DELTA.T in the expression for .DELTA.p
above has been neglected and introduces an error. It is
proportional to the average velocity and the deviation in step
time. As a refinement, it could be included in the estimate of
.DELTA.p.
[0083] Since the orientation estimate contains errors, there is
always some form of gravity leakage into the horizontal
accelerations (i.e. the horizontal accelerations will include some
component due to gravity) and double integration may induce large
errors in the estimated step size. One way to suppress this leakage
is by band-pass filtering (or high-pass filtering) the signals from
the accelerometer 6, preferably using a linear phase filter so that
the wave form of the signal is maintained. Typical cut-off
frequencies are 0.1 Hz and 20-40 Hz (where the actual application
of the upper cut-off frequency also depends on the used sampling
frequency). This band-pass filtering is not required.
[0084] A measure that has been found to be particularly effective
is the de-trending per integration interval (step or stride) of the
acceleration. This amounts to requiring that the average
acceleration (from the start heel strike to the end heel strike) is
zero. In other words, the velocity after integration equals the
velocity at the start of integration (which is zero). The
de-trended acceleration is obtained by subtracting the integrated
acceleration, divided by the step duration, from the measured
acceleration. The de-trended acceleration is used to compute the
step size as described above.
[0085] Assuming no errors in the coordinate system transformation,
for example due to inhomogeneity in the direction and size of the
geomagnetic field, this integration yields the direction of a step
in terms of north-south and east-west. However, for gait stability
it is necessary to examine the motion in forward and lateral
directions. This is solved in the following way.
[0086] The start position (which is the origin, by definition) and
the end position are computed as explained above. Their difference
spans a 2D vector in the horizontal plane. Then, the forward step
size is defined as the norm of this 2D vector, i.e. as the distance
of the end position from the start position (it should be noted
that, as a consequence, the above .DELTA.p are always positive).
Subsequently, a straight line is defined between the start position
and the end position and the distance is determined between this
line and each point from the double integration of the
accelerometer measurements during the step. The maximum distance in
this series is taken as the lateral step size.
[0087] Step time follows as the duration between the estimated step
boundaries, and velocity as the ratio between step size and step
time (in the event that the device 2 is located on a foot of the
user 4).
[0088] While stride sizes are computed when the device 2 is on the
lower body of the user 4, step size can be computed when the device
2 is on the upper body of the user 4, yielding twice as fast an
average.
[0089] It will be appreciated by those skilled in the art that
alternative methods for determining step size can be used that do
not require the double integration of the horizontal acceleration
measurements. These methods include the inverted pendulum model
[Zijlstra & H of, Displacement of the pelvis during human
walking, Gait and Posture 6, 1997, 249-267] and the 4.sup.th power
root of the difference between maximum and minimum vertical
acceleration [Weinberg, Using the ADXL202 in Pedometer and Personal
Navigation Applications, Application Note AN-602, Analog Devices,
2002].
[0090] Although the invention has been described in terms of a
pendant to be worn around the neck of a user 4, it will be
appreciated that the invention can be implemented in alternative
forms that are to be worn on different parts of the body of the
user 4. Of course, in these embodiments, it will be necessary to
modify the processing used to determine the gait parameters from
the sensor measurements, but these modifications will be readily
apparent to a person skilled in the art based on the description
provided above.
[0091] In the embodiment described with reference to FIG. 2, the
collection and processing of the sensor measurements is performed
in a single unit. However, in alternative embodiments, the
processing of the measurements can be performed in a unit that is
remote from the sensors, in which case the fall prevention device 2
will comprise a sensor unit to be worn by the user 4 that transmits
the sensor measurements to the remote unit. In this embodiment,
there is no need for the sensor unit to include a dedicated
processor.
[0092] There is therefore provided a method and device that can
determine an instantaneous fall risk for a user.
[0093] It will be appreciated that the algorithms described above
that are used to determine various gait parameters can be used in
applications other than fall prevention. For example, they can be
used in activity monitoring and fitness applications such as
endurance coaching (for example to support the keeping of cadence
during jogging). The algorithms may be used in step counters or
devices that support injury prevention, for example during jogging.
Further the algorithms may be used in a device or system that uses
gait parameters as a biometric for identifying an individual.
[0094] While the invention has been illustrated and described in
detail in the drawings and foregoing description, such illustration
and description are to be considered illustrative or exemplary and
not restrictive; the invention is not limited to the disclosed
embodiments.
[0095] Variations to the disclosed embodiments can be understood
and effected by those skilled in the art in practicing the claimed
invention, from a study of the drawings, the disclosure, and the
appended claims. In the claims, the word "comprising" does not
exclude other elements or steps, and the indefinite article "a" or
"an" does not exclude a plurality. A single processor or other unit
may fulfill the functions of several items recited in the claims.
The mere fact that certain measures are recited in mutually
different dependent claims does not indicate that a combination of
these measures cannot be used to advantage. A computer program may
be stored/distributed on a suitable medium, such as an optical
storage medium or a solid-state medium supplied together with or as
part of other hardware, but may also be distributed in other forms,
such as via the Internet or other wired or wireless
telecommunication systems. Any reference signs in the claims should
not be construed as limiting the scope.
* * * * *