U.S. patent application number 16/779747 was filed with the patent office on 2020-06-04 for automated near-fall detector.
This patent application is currently assigned to The Medical Research, Infrastructure and Health Services Fund of the Tel Aviv Medical Center. The applicant listed for this patent is The Medical Research, Infrastructure and Health Services Fund of the Tel Aviv Medical Center. Invention is credited to Nir GILADI, Jeffrey M. HAUSDORFF.
Application Number | 20200170548 16/779747 |
Document ID | / |
Family ID | 42790696 |
Filed Date | 2020-06-04 |
United States Patent
Application |
20200170548 |
Kind Code |
A1 |
HAUSDORFF; Jeffrey M. ; et
al. |
June 4, 2020 |
AUTOMATED NEAR-FALL DETECTOR
Abstract
A method of gait data collection, the method comprising
collecting movement data, determining from the data a movement
parameter that includes a third order derivative of position,
comparing the movement parameter with a threshold value, and
counting at least a near fall if the movement parameter exceeds the
threshold value.
Inventors: |
HAUSDORFF; Jeffrey M.;
(Hashmonaim, IL) ; GILADI; Nir; (Tel-Aviv,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Medical Research, Infrastructure and Health Services Fund of
the Tel Aviv Medical Center |
Tel-Aviv |
|
IL |
|
|
Assignee: |
The Medical Research,
Infrastructure and Health Services Fund of the Tel Aviv Medical
Center
Tel-Aviv
IL
|
Family ID: |
42790696 |
Appl. No.: |
16/779747 |
Filed: |
February 3, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13380863 |
Dec 26, 2011 |
10548512 |
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PCT/IL10/00505 |
Jun 24, 2010 |
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16779747 |
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61219811 |
Jun 24, 2009 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/1117 20130101;
A61B 2562/0219 20130101; A61B 5/6831 20130101; A61B 5/4082
20130101 |
International
Class: |
A61B 5/11 20060101
A61B005/11; A61B 5/00 20060101 A61B005/00 |
Claims
1-20. (canceled)
21. A method of determining a near fall event, wherein a user
recovers from a momentary loss of balance without falling, the
method comprising: electronically collecting movement data using a
detector configured to measure acceleration of the user's body;
using a processor in communication with said detector,
electronically determining at least one movement parameter value
from said data collected by said detector, wherein said at least
one movement parameter value is based on an acceleration pattern;
and using said processor, processing said at least one movement
parameter value determined from said data collected using said
detector to identify the near fall event, based on at least one
threshold value.
22. A method according to claim 21, wherein said at least one
movement parameter value includes a measure of maximum
acceleration.
23. A method according to claim 21, wherein the method comprises
determining a fall.
24. A method according to claim 21, wherein determining comprises
matching a pattern with respect to time of the movement data with a
reference pattern.
25. A method according to claim 24, wherein the reference pattern
represents one of: a proper gait pattern, an improper gait pattern,
and a gait pattern exhibiting at least one near fall event.
26. A method according to claim 24, wherein the matching classifies
the data as exhibiting a fall, a near fall event, or lack
thereof.
27. A method according to claim 24, wherein the matching comprises
at least one of correlation, cross-correlation, wavelets matching
or neural networks or a combination thereof.
28. A method according to claim 21, wherein said processing said at
least one movement parameter value includes electronically
comparing said at least one movement parameter value with the at
least one threshold value, and wherein said electronically
comparing comprises comparing said measure of movement in a
substantially vertical direction with said at least one threshold
value to identify the near fall event.
29. A method according to claim 21, wherein determining at least
one movement parameter value from said data further includes
determining a second movement parameter value; wherein said
processing said at least one movement parameter value includes
processing at least a said second movement parameter value; wherein
comparing said at least one movement parameter value further
includes comparing said second movement parameter value with a
second threshold value; and wherein said method further includes
counting at least the near fall event if a first movement parameter
value exceeds a first threshold value and said second movement
parameter value exceeds said second threshold value.
30. A method according to claim 29, wherein said second movement
parameter value includes one of the group consisting of; a rate of
change of acceleration, an angular velocity, an anterior-posterior
acceleration, and a medio-lateral acceleration.
31. A method according to claim 21, wherein said at least one
threshold value is a predetermined value.
32. A method according to claim 21, wherein said method further
includes storing a count of near fall events to provide at least
one of: a quantitative measure of effectiveness of therapeutic
interventions and quantifiable parameters for assessing a
person.
33. A method according to claim 21, wherein said movement data
includes cyclic acceleration data; and wherein said electronically
determining the at least one movement parameter value comprises:
determining from said acceleration data periods of cycles; and
identifying a gait irregularity when a period of a cycle exceeds a
threshold.
34. A method according to claim 33, wherein said cyclic
acceleration data includes peaks; wherein each cycle includes a
cycle shape; and wherein said electronically determining comprises
at least one of: (a) determining from said acceleration data
periods between said peaks; and identifying a gait irregularity
when at least one of: a period between said peaks exceeds a
threshold; and a cycle shape varies above a threshold; (b)
determining from said acceleration data a cross-correlation between
cycles; and identifying a gait irregularity when the
cross-correlation between cycles exceeds a threshold; and (c)
determining from said data an acceleration frequency spread; and
identifying an irregularity of a gait from said acceleration
frequency spread.
35. A method according to claim 21, wherein said at least one
movement parameter value relates to movement in at least one of: a
substantially an anterior-posterior direction and a substantially
vertical direction.
36. A method according to claim 21, wherein said using the
processor includes electronically determining the at least one
movement parameter value from collected acceleration data
alone.
37. A method according to claim 21, wherein said momentary loss of
balance is during a gait.
38. A method according to claim 21, wherein said at least one
movement parameter value determined from said data collected
comprises a plurality of movement parameter values including at
least one movement parameter value related to a movement parameter
in a substantially vertical direction; wherein said processing said
at least one movement parameter value includes electronically
determining, using said processor, from said data at least one
irregularity of a gait, including identifying the near fall event
during the gait, said electronically determining including
electronically comparing each of said plurality of movement
parameter values with an associated threshold value, wherein said
electronically comparing comprises comparing said measure of
maximum acceleration with said threshold value to identify the near
fall event during the gait, including comparing said at least one
movement parameter value related to the movement parameter in the
substantially vertical direction determined from said data
collected using said detector with a threshold value, to indicate
the near fall event when each said movement parameter value related
to the movement parameter in the substantially vertical direction
exceeds an associated threshold value; and wherein, if a
predetermined combination of comparisons indicates the near fall
event, said method further includes electronically storing a count
of the near fall event in a memory.
39. A method according to claim 28, the method comprising:
electronically recording a magnitude of said near fall event.
40. A method according to claim 38, wherein said predetermined
combination of movement parameters is a majority of said plurality
of movement parameters; and wherein said electronically storing the
count comprises electronically counting at least a near fall event
if a majority of said combination of comparisons indicates a near
fall event.
41. A method according to claim 21, wherein said movement data
includes a measure of maximum acceleration; wherein said method
further includes using the processor in communication with said
detector to electronically extract an indicator indicating a loss
of control from said data collected, wherein said indicator
indicating the loss of control includes at least one movement
parameter value which exceeds a threshold value, wherein said
electronically extracting an indicator comprises comparing said
measure of maximum acceleration with said threshold value to
identify a near fall event; and wherein, if said indicator
indicates said loss of control, said method further includes:
electronically storing a count of near fall events in a memory; and
electronically recording a date or time for each said near fall
event, in said memory.
Description
RELATED APPLICATIONS
[0001] This Application is a continuation of U.S. patent
application Ser. No. 13/380,863, filed on Dec. 26, 2011 which is a
National Phase of PCT Patent Application No. PCT/IL2010/000505
having International Filing Fate of Jun. 24, 2010, which claims the
benefit of priority of U.S. Provisional Patent Application No.
61/219,811 filed on Jun. 24, 2009.
[0002] The contents of the above Applications are all incorporated
herein by reference.
FIELD AND BACKGROUND OF THE INVENTION
[0003] The present invention, in some embodiments thereof, relates
to motion detection, and more particularly, but not exclusively, to
a system useful for identifying gait or fall related motion.
[0004] A public health issue of concern is the incidence of falls,
in which a person falls to the ground from an upright position
while standing or walking. The problem of falls affects the elderly
in general, and is of particular concern for older persons and
others who have a movement disorder or other illness that affects
balance and motor control, such as Parkinson's disease.
[0005] The effect of a fall on an elderly person can be
particularly serious since many elderly people have weak or brittle
bones, and are generally further weakened by other illnesses and
the effects of aging. In some cases a fall causes the death of a
person, either at the time of the fall or indirectly as a result of
the injuries sustained. The type of injuries commonly experienced
may include one or more of: a broken or fractured hip and other
bones, head injuries, internal and external bleeding, and soft
tissue and skin damage. The patient will most likely suffer a great
deal of pain and may require hospitalization. In addition, he or
she may face the prospect of long term or permanent loss of
mobility, since their age and condition may mean that the injuries
will take a long time to heal or may never heal completely. The
patient may be plagued by fear of a recurrence, so that their
mobility and confidence is further compromised. Accordingly, even
if death is avoided, the injuries suffered from a fall can be
devastating to the person's physical and mental well-being.
[0006] Various systems have been proposed to automatically identify
falls, so that an action can be triggered to help alleviate the
damage caused by the fall. For example, upon detecting that a fall
has occurred, a system could notify a relative or doctor to check
up on the patient. Dinh et al. in "A Fall Detection and Near-Fall
Data Collection System" (Microsystems and Nanoelectronics Research
Conference (MNRC), October 2008) describe a wearable device
containing a 3-axis accelerometer, a 2-axis gyroscope, and a heart
beat detection circuit. Data collected from the sensors is beamed
wireles sly to a receiver connected to a computer. The researchers
observed that combining the accelerometer data with the gyroscope
data produced good results in identifying whether a fall had
occurred.
[0007] Bourke et al. in "Distinguishing Falls from Normal ADL using
Vertical Velocity Profiles", (IEEE Conference on Engineering in
Medicine and Biology, August 2007) observe that a single threshold
applied to the vertical velocity profile of the trunk may
distinguish falls from activities of daily living (ADL).
[0008] In another paper, Wu and Xue in "Portable Preimpact Fall
Detector With Inertial Sensors" (IEEE Transactions on Neural
Systems and Rehabilitation Engineering, April 2008), describe a
portable preimpact fall detector that detects a pending fall at its
inception, so that an inflatable hip protector can be triggered in
time to break the fall. The detector was equipped with an
orientation or inertial sensor that included triaxial
accelerometers and triaxial angular rate sensors, and used a
detection algorithm based on the inertial frame velocity profile of
the body. In particular, the inertial frame vertical velocity
magnitude was measured and compared to a threshold value to
identify a fall. The system was tested in a variety of activities
to determine the threshold level of inertial frame vertical
velocity magnitude.
SUMMARY OF THE INVENTION
[0009] An aspect of some embodiments of the invention relates to
detection of gait irregularity and/or of near fall.
[0010] In an exemplary embodiment of the invention, a near fall is
characterized based on its vertical acceleration profile, for
example, the rate of change of vertical acceleration being above a
threshold. Optionally, a comparison to a threshold uses inexact
methods, for example fuzzy logic. Optionally or alternatively, the
comparison is of a function of acceleration to a function of the
threshold. Optionally, the threshold is dynamic, for example, as a
function of context of the gait and/or of recent movement
parameters.
[0011] In some exemplary embodiments of the invention, gait
irregularity is characterized based on vertical acceleration.
Typically, corresponding to gait's steps movements, movement's
acceleration signal exhibits a generally cyclic pattern with peaks.
In some embodiments, irregularity is determined when the periods of
the cycles (e.g. between peaks) vary above a threshold. In some
embodiments, the irregularity is determined when the shape of the
cycles vary above a threshold, where the variability of the shape
is determined, for example, by variations in cross-correlation
between the cycles. In some embodiments, the irregularity is
determined by a frequency spread of the acceleration signal, such
as obtained with a Fourier transform.
[0012] Optionally, a comparison to a threshold uses inexact
methods, for example fuzzy logic. Optionally or alternatively, the
comparison is of a function of acceleration to a function of the
threshold. Optionally, the threshold is dynamic, for example, as a
function of context of the gait and/or of recent movement
parameters.
[0013] In some embodiments, a combination of two or more of the
methods, i.e. cycles time, cycles shape and frequency spread, is
used to determine irregularity.
[0014] In some embodiments, the irregularity is checked along a
certain or determined time. Optionally, the irregularity is checked
within a moving window of a certain or determined time.
[0015] Alternatively or additionally to evaluation of near fall
and/or gait irregularity by parameters or values derived from the
acceleration, in some exemplary embodiments of the invention
determination of near fall and/or gait irregularity is based on the
waveform of the acceleration (or other movement signals).
[0016] In some embodiments, the waveform of gait acceleration over
a certain period is evaluated against a reference waveform or
library of waveforms of gait acceleration, and near fall and/or
gait irregularity is determined or classified according to a degree
of matching or mismatching with the reference waveform(s).
[0017] In some embodiments, the waveform of a subject is matched
against a reference waveform by methods of pattern matching such as
correlation or cross-correlation or wavelet matching or machine
learning (e.g. neural networks) or any combination of methods of
the art.
[0018] In some exemplary embodiments of the invention, a derivative
of the accelerations is used to determine near fall and/or gait
irregularity. Optionally, other parameters such as angular velocity
or tilt are used.
[0019] An aspect of some embodiments of the invention relates to
gait regulation assistance. In some embodiments, irregularity in
gait is detected, such as described above. Responsive to a
determined gait irregularity of a person, the person is prompted,
such as by audio message or tactile incitement, to adjust and/or
stabilize the gait (cuing signals).
[0020] An aspect of some embodiments of the invention relates to
enhancing a Timed Up and Go (TUG) test to assess the tendency of a
person to fall (persons prone to fall). In some embodiments, the
enhancement is based on the rate of change of position during
sitting or rising (jerks), such as a time derivative of the
vertical acceleration. In some embodiments, the tendency to falling
is assessed when the rate of change of the acceleration is above a
threshold. In some embodiments, the threshold is based on the rate
of change of acceleration of healthy person or persons. Optionally
or additionally, the threshold is based on the physiological state
of the person being assessed, such as neurological disorder.
[0021] There is provided in accordance with an exemplary embodiment
of the invention, a method of gait data collection, the method
comprising:
[0022] A method of gait data collection, the method comprising:
[0023] collecting movement data, and [0024] determining from said
data at least one irregularity of the gait.
[0025] In some embodiments, an irregularity comprises a near
fall.
[0026] In some embodiments, an irregularity comprises a fall.
[0027] In some embodiments, determining comprises determining from
said data a movement parameter that includes a third order
derivative of position, and counting at least a near fall based on
said movement parameter.
[0028] In some embodiments, determining comprises matching the
pattern with respect to time of the movement data with a reference
pattern.
[0029] In some embodiments, the reference pattern represents proper
gait pattern.
[0030] In some embodiments, the reference pattern represents
improper gait pattern.
[0031] In some embodiments, the reference pattern represents a gait
pattern exhibiting at least one near fall.
[0032] In some embodiments, the matching classified the data as
exhibiting fall, near fall or lack thereof.
[0033] In some embodiments, wherein the matching comprises at least
one of correlation, cross-correlation, wavelets matching or neural
networks or a combination thereof.
[0034] In an exemplary embodiment of the invention, the method
comprises comparing said movement parameter with a threshold value
to identify a near fall.
[0035] In an exemplary embodiment of the invention, said movement
parameter comprises a difference between a maximum acceleration
derivative and a minimum acceleration derivative. Optionally, said
movement parameter relates to movement in substantially a vertical
direction.
[0036] In an exemplary embodiment of the invention,
[0037] determining from said data further includes determining a
second movement parameter,
[0038] comparing said movement parameter further includes comparing
said second movement parameter with a second threshold value,
and
[0039] counting at least a near fall comprises counting at least a
near fall if said movement parameter exceeds said threshold value
and said second movement parameter exceeds said second threshold
value.
[0040] In an exemplary embodiment of the invention, said second
movement parameter includes a second order derivative of position.
Optionally or alternatively, said movement parameter and said
second movement parameter relate to movement in substantially a
vertical direction.
[0041] In an exemplary embodiment of the invention, said threshold
value is a predetermined value.
[0042] In an exemplary embodiment of the invention, said threshold
value is a continuously updated function of said movement
parameter. Optionally, said function is a mean of said movement
parameter plus a multiple of a standard deviation of said movement
parameter.
[0043] In an exemplary embodiment of the invention, determining a
movement parameter comprises collecting acceleration data and
taking a derivative of said acceleration data with respect to
time.
[0044] In an exemplary embodiment of the invention, determining a
movement parameter comprises collecting velocity data and taking a
second order derivative of said velocity data with respect to
time.
[0045] In an exemplary embodiment of the invention, determining a
movement parameter comprises collecting position data and taking a
third order derivative of said position data with respect to
time.
[0046] In an exemplary embodiment of the invention, said count of
at least a near fall provides a quantitative measure of
effectiveness of therapeutic interventions.
[0047] There is provided in accordance with an exemplary embodiment
of the invention, a method of gait data collection, the method
comprising:
[0048] collecting movement data,
[0049] determining from said data a plurality of movement
parameters, each of said movement parameters including at least one
of a second order derivative of position and a third order
derivative of position,
[0050] comparing each of said movement parameters with an
associated threshold value, and
[0051] counting at least a near fall if a predetermined combination
of movement parameters from said plurality of movement parameters
exceeds their associated threshold value.
[0052] There is provided in accordance with an exemplary embodiment
of the invention, a method of gait data collection, the method
comprising:
[0053] collecting movement data,
[0054] extracting from said movement data an indicator indicating a
loss of control,
[0055] counting at least a near fall if said indicator indicates
said loss of control.
[0056] There is provided in accordance with an exemplary embodiment
of the invention, a device to detect falling body movement, the
device comprising:
[0057] a sensor operatively connected to said body and responsive
to movement of said body, and
[0058] a processor to receive movement data from said sensor and to
process said movement data to identify events that are at least
near falls.
[0059] In an exemplary embodiment of the invention, said processor
is configured to log a record of events that are at least near
falls. Optionally or alternatively, said sensor is responsive to
movement of said body in substantially a vertical direction.
Optionally or alternatively, said sensor is responsive to
acceleration of said body.
[0060] In an exemplary embodiment of the invention, the device
includes a user interface to communicate with a user of said
device.
[0061] In an exemplary embodiment of the invention, said sensor and
said processor are enclosed in a housing.
[0062] In an exemplary embodiment of the invention, said processor
is located remote from said sensor.
[0063] In an exemplary embodiment of the invention, the device
includes a radio transmitter operatively connected to said sensor
and a radio receiver operatively connected to said processor,
[0064] wherein said transmitter and said receiver are configured to
enable said processor to receive movement data from said sensor in
real time.
[0065] There is provided in accordance with an exemplary embodiment
of the invention a method for assisting a person's gait,
comprising:
[0066] (a) detecting, based on time derivation of gait movements,
irregularity in the gait; and
[0067] (b) providing gait regulating cueing signals.
[0068] There is provided in accordance with an exemplary embodiment
of the invention an apparatus for assisting a person's gait,
comprising:
[0069] (a) a sensor operatively connected to the person and
responsive to movement of said person;
[0070] (b) a processor adapted to receive movement data from said
sensor and to process said movement data to detect irregularity in
the movement; and
[0071] (c) at least one device operable to provide cuing signals
responsive to detected irregularity.
[0072] In some embodiments, the signals are at least one of
audible, tactile or visual.
[0073] There is provided in accordance with an exemplary embodiment
of the invention a method for augmenting a Timed Up and Go test,
comprising:
[0074] (a) determining rate of change of acceleration of movement
about at least one of seating or rising; and
[0075] (b) screening, based on the rate of change of the
acceleration, a tendency to fall.
[0076] In some embodiments, the screening is determined of a rate
larger than that of a healthy person.
[0077] In some embodiments, the screening is determined when the
rate of the acceleration of a sitting movement is about 1 g/sec
[0078] In some embodiments, the screening is determined when the
rate of the acceleration of a rising movement is about 2 g/sec
[0079] Unless otherwise defined, all technical and/or scientific
terms used herein have the same meaning as commonly understood by
one of ordinary skill in the art to which the invention pertains.
Although methods and materials similar or equivalent to those
described herein can be used in the practice or testing of
embodiments of the invention, exemplary methods and/or materials
are described below. In case of conflict, the patent specification,
including definitions, will control. In addition, the materials,
methods, and examples are illustrative only and are not intended to
be necessarily limiting.
[0080] Implementation of the method and/or system of embodiments of
the invention can involve performing or completing selected tasks
manually, automatically, or a combination thereof. Moreover,
according to actual instrumentation and equipment of embodiments of
the method and/or system of the invention, several selected tasks
could be implemented by hardware, by software or by firmware or by
a combination thereof using an operating system.
[0081] For example, hardware for performing selected tasks
according to embodiments of the invention could be implemented as a
chip or a circuit. As software, selected tasks according to
embodiments of the invention could be implemented as a plurality of
software instructions being executed by a computer using any
suitable operating system. In an exemplary embodiment of the
invention, one or more tasks according to exemplary embodiments of
method and/or system as described herein are performed by a data
processor, such as a computing platform for executing a plurality
of instructions. Optionally, the data processor includes a volatile
memory for storing instructions and/or data and/or a non-volatile
storage, for example, a magnetic hard-disk and/or removable media,
for storing instructions and/or data. Optionally, a network
connection is provided as well. A display and/or a user input
device such as a keyboard or mouse are optionally provided as
well.
BRIEF DESCRIPTION OF THE DRAWINGS
[0082] Some embodiments of the invention are herein described, by
way of example only, with reference to the accompanying drawings.
With specific reference now to the drawings in detail, it is
stressed that the particulars shown are by way of example and for
purposes of illustrative discussion of embodiments of the
invention. In this regard, the description taken with the drawings
makes apparent to those skilled in the art how embodiments of the
invention may be practiced.
[0083] In the drawings:
[0084] FIGS. 1A, 1B, and 1C are schematic views of a person
walking, having a near fall, and recovering to resume walking,
respectively, while wearing an automated near-fall detector, in
accordance with an embodiment of the invention;
[0085] FIGS. 2A, 2B, and 2C are schematic views of the automated
near-fall detector of FIG. 1, in accordance with several
embodiments of the invention;
[0086] FIGS. 3A and 3B are flow charts describing a method of gait
data collection, in accordance with an embodiment of the
invention;
[0087] FIG. 4 shows graphs of derived parameters Vertical Maximum
Acceleration and Vertical Maximum Peak to Peak Derivative, in
accordance with an embodiment of the invention;
[0088] FIG. 5 shows exemplary charts of stride acceleration and
frequency spread of a healthy person and a person with Parkinson
disease, respectively; and
[0089] FIG. 6 shows exemplary charts of Timed Up and Go (TUG) of
healthy person and a person prone to falling, respectively.
DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION
[0090] The present invention, in some embodiments thereof, relates
to motion detection, and more particularly, but not exclusively, to
a system useful for identifying gait or fall related motion.
[0091] Before explaining at least one embodiment of the invention
in detail, it is to be understood that the invention is not
necessarily limited in its application to the details of
construction and the arrangement of the components and/or methods
set forth in the following description and/or illustrated in the
drawings and/or the Examples. The invention is capable of other
embodiments or of being practiced or carried out in various
ways.
[0092] In an exemplary embodiment of the invention, a near fall is
characterized based on its vertical acceleration profile, for
example, the rate of change of vertical acceleration being above a
threshold. Optionally, a comparison to a threshold uses inexact
methods, for example fuzzy logic. Optionally or alternatively, the
comparison is of a function of acceleration to a function of the
threshold. Optionally, the threshold is dynamic, for example, as a
function of context of the gait and/or of recent movement
parameters.
[0093] In some exemplary embodiments of the invention, gait
irregularity is characterized based on vertical acceleration.
Typically, corresponding to gait's steps movements, movement's
acceleration signal exhibits a generally cyclic pattern with peaks.
In some embodiments, irregularity is determined when the periods of
the cycles (e.g. between peaks) vary above a threshold. In some
embodiments, the irregularity is determined when the shape of the
cycles vary above a threshold, where the variability of the shape
is determined, for example, by variations in cross-correlation
between the cycles. In some embodiments, the irregularity is
determined by a frequency spread of the acceleration signal, such
as obtained with a Fourier transform.
[0094] Optionally, a comparison to a threshold uses inexact
methods, for example fuzzy logic. Optionally or alternatively, the
comparison is of a function of acceleration to a function of the
threshold. Optionally, the threshold is dynamic, for example, as a
function of context of the gait and/or of recent movement
parameters.
[0095] In some embodiments, a combination of two or more of the
methods, i.e. cycles time, cycles shape and frequency spread, is
used to determine irregularity.
[0096] In some embodiments, the irregularity is checked along a
certain or determined time. Optionally, the irregularity is checked
within a moving window of a certain or determined time.
[0097] Alternatively or additionally to evaluation of near fall
and/or gait irregularity by parameters or values related to the
size of the acceleration or other movement signal (i.e., above or
below a threshold), in some exemplary embodiments of the invention
determination of near fall and/or gait irregularity is based on the
waveform of the acceleration (or movement signal).
[0098] In some embodiments, the waveform of gait acceleration over
a certain period is evaluated against a reference waveform(s) of
gait acceleration, and near fall and/or gait irregularity is
determined or classified according to a degree of matching or
mismatching with the reference waveform. In some embodiments, the
classification comprises a fall or near fall event or the lack
thereof.
[0099] In some embodiments, the waveform of acceleration of the
gait of a subject is matched against a reference waveform by
methods of pattern matching such as correlation or
cross-correlation or wavelet matching or machine learning (e.g.
neural networks) or any combination of methods of the art.
Optionally the determination or classification of gait irregularity
and/or near fall by matching methods is augments by other methods
such as fuzzy logic.
[0100] When a subject's waveform sufficiently deviates from a
reference signal representing a proper gait, the subject is
determined to exhibit irregular gait. Optionally, by features
matching between the waveforms a near fall is determined if
characteristic features are different between the waveforms.
[0101] When a subject's waveform sufficiently matches a reference
signal representing an improper gait, the subject is determined to
exhibit irregular gait. Optionally, when the subject's waveform
sufficiently matches a waveform with near fall events, the subject
is determined to exhibit near fall behavior. Optionally, by
features matching between the waveforms a near fall is determined
if characteristic features are similar between the waveforms.
[0102] For example, the acceleration waveform of a subject is
matched against a waveform representing a healthy gait, and if the
waveforms deviated above a threshold the subject's gait is
determined to be irregular. Optionally, features of the waveforms
are matched and based on dissimilarities such as missing or
different features between the waveforms, the subject's gait is
determined to exhibit near fall behavior.
[0103] As another example, the acceleration waveform of a subject
is matched against a waveform representing a person having improper
gait. If, based on a threshold or other measures, the waveforms are
sufficiently close and/or exhibit similar features the subject's
gait is determined to be irregular or having near fall
characteristics. Optionally, features of the waveforms are matched
and according to some measures, such as missing or different
features between the waveforms, the subject's gait is determined to
exhibit near fall behavior.
[0104] In some embodiments, a `healthy` or `proper` reference
waveform is based on the gait of healthy persons, optionally of
about the age of the subject being evaluated. For example,
acceleration waveforms of healthy persons are collected and
combined, such as by scaling and averaging or by any other methods,
to provide a representative waveform of proper or regular gait.
Optionally, the representative waveform is based, at least
partially, on the gait acceleration of other neurologically
diseased while they exhibit regular gait. Optionally, the
representative waveform is based, at least partially, on synthetic
waveform computed to represent a proper gait.
[0105] In some embodiments, an `ill` or `improper` reference
waveform is based on the gait of neurologically diseased persons,
optionally of about the age and/or disorder of the subject being
evaluated. For example, acceleration waveforms of persons
exhibiting irregular or disordered or near fall behavior are
collected and combined, such as by scaling and averaging or by any
other methods, to provide a representative waveform of improper
gait. Optionally, the representative waveform is based, at least
partially, on the gait acceleration of other neurologically
diseased while they exhibit irregular gait. Optionally, the
representative waveform is based, at least partially, on synthetic
waveform computed to represent an improper gait.
[0106] In some embodiments, in order to improve or refine the
evaluation of a subject's waveform, the waveform is matched against
a plurality of reference waveforms, either proper and/or improper
waveforms. For example, the subject's waveform is matched against
both proper and improper references and the irregularity or near
fall characteristics are determined by a combination of the
matching results.
[0107] In some embodiments, the representative waveforms are
updated from time to time to form a library or repository of
reference waveforms.
[0108] In some exemplary embodiments of the invention a derivative
of the accelerations are used to determine near fall and/or gait
irregularity. Optionally, other parameters such as angular velocity
or tilt are used such as to refine the determination of fall and/or
gait irregularity.
[0109] In some embodiments, the presence or absence of a near fall
or other gait irregularity is made by combining methods based on
pattern recognition of the waveforms with those that are based on
threshholding of the acceleration jerk or other derived movement
parameters.
[0110] In some embodiments, irregularity in gait is detected, such
as described above. Responsive to a determined gait irregularity of
a person, the person is prompted, such as by audio message or
tactile incitement, to adjust and/or stabilize the gait (cuing
signals).
[0111] In some embodiments, a Timed Up and Go (TUG) test to assess
the tendency of a person to fall (persons prone to fall) is
enhanced. In some embodiments, the enhancement is based on the rate
of change of position during sitting or rising (jerks), such as a
time derivative of the vertical acceleration. In some embodiments,
the tendency to falling is assessed when the rate of change of the
acceleration is above a threshold. In some embodiments, the
threshold is based on the rate of change of acceleration of healthy
person or persons. Optionally or additionally, the threshold is
based on the physiological state of the person being assessed, such
as neurological disorder.
1. Overview
[0112] FIGS. 1A, 1B, and 1C show a near fall detector device 20,
according to an embodiment of the invention, in a typical
application being used by a walking person 22. Person 22 may be a
man or woman of any age and of any physical condition. In this
example near fall detector 20 is a device attached to a belt 24
worn by person 22. As will be discussed in greater detail below,
near fall detector 20 optionally uses signal processing methods to
monitor the quality of a walking person's gait or ambulatory
movement, and responds or records in some fashion in the event that
the person's walk is interrupted by a near fall or a real fall.
Optionally, detector 20 is also capable of detecting a fall or near
fall that may be experienced by a person that is standing or
sitting.
[0113] In FIG. 1A person 22 is shown walking in a normal fashion.
At some later point in time, as shown in FIG. 1B, person 22
experiences a near fall. The near fall, also called a stumble or
misstep, is a momentary loss of balance by the person from which
the person recovers. By contrast, in a real or actual fall (or just
"fall") the person does not recover and continues to fall until he
or she comes to rest on the ground, floor, or other lower level.
FIG. 1B illustrates some characteristics of an example of a near
fall. As may be seen, the person's legs have slipped so they are no
longer directly underneath, and accordingly the person's center of
gravity 26 has moved off center so that the person experiences a
sensation of loss of balance. As most people may relate, the
person's arms thrust out to compensate in an effort to recover
balance and avoid falling. In this example person 22 is successful
at avoiding the fall, and is shown in FIG. 1C at a later point in
time resuming his or her walk. Near fall detector 20 however has
detected the incident shown in FIG. 1B. This is indicated, by way
of example, in the enlarged representation of the detector in inset
28 in FIG. 1C, which shows detector 20 displaying the words "Near
Fall". In other examples, detector 20 might log a record of the
incident and not display anything, or detector 20 might query the
user to confirm the near fall.
[0114] The near fall illustrated in FIG. 1B could occur in any
direction, have a degree of magnitude or force behind it, and be
due to any cause. For example, the near fall could be in a forward
direction (as shown in the figure), as might occur due to tripping.
Other types of near falls include, for example, a backward near
fall caused by a slippery floor, or a sideways near fall caused by
a misstep. The person might also have a near fall directed straight
down, for example due to fainting. Near falls may be caused by
external circumstances, such as an unexpected obstacle or slippery
surface, or by circumstances internal to the person, such as by
fainting, general weakness, or a movement disorder. In many cases
the near fall is caused by a combination of the two. For example,
an obstacle may be encountered that a healthy person would easily
avoid, but that precipitates a near fall in an older person with
poor eyesight and a slow reaction time. In an exemplary embodiment
of the invention, near fall detection is practiced in
clinical/diagnostic settings, where a patient is given a task, such
as an obstacle course, and his performance thereon monitored.
[0115] In addition to detecting the incident of a near fall, near
fall detector 20, in some embodiments of the invention, may also
detect the magnitude and/or direction of the near fall. Further, as
will be discussed in greater detail below, near fall detector 20 in
some embodiments of the invention performs gait data collection
and/or includes an algorithm configured to detect the occurrence of
actual falls as well as near falls.
[0116] The inventors have realized that many people who have
experienced actual falls, or that are prone to falling, may in fact
only fall a relatively small number of times. This does not detract
from the seriousness of the problem, since all it takes is one bad
fall to seriously injure a person. However, it does suggest that
for such people it can be difficult to collect meaningful data to
prevent future falls, especially if their memory is faulty and/or
if interrogation occurs at a time after such an event. The
inventors have further observed that people at risk of falling
often have multiple near falls for every actual fall that they
experience, and also prior to their falling for the first time. In
an exemplary embodiment of the invention, the detection of near
falls provides insight into a person's condition that may assist in
the diagnosis and prevention of subsequent real falls by that
person.
[0117] As discussed in greater detail below, near fall data can
provide quantifiable parameters whose value can be used to better
assess the person at risk. Additionally, when combined with data on
a person's actual falls (which as noted can also be obtained from
detector 20 of some embodiments of the present invention), a
diagnostician can obtain a ratio (or other relationship) of actual
falls to near falls and acquire a more complete picture of the
person's condition. Through a review of the pattern of near fall
frequency and optionally other parameters such as magnitude and
direction of the near falls that might precede a full fall, near
fall detector 20, in an embodiment of the invention, may be useful
to alert a person and/or the person's physician that the person is
at risk of falling. The person may then respond by wearing
protective padding r other safety equipment, for example, or by
taking other suitable precautions that prevent a fall from
happening that would otherwise have occurred. Near fall data may
also provide a quantitative measure that can be used to evaluate
the effectiveness of therapeutic interventions.
[0118] Near fall detector 20 of the present invention, in some
embodiments, can be configured to automatically record and/or
report the number of instances of near falls, as well as details of
each near fall such as one or more of the date and time at which it
occurred, its magnitude, direction, its location and/or movements
before or after the fall (e.g., indicating stair climbing, fast
walking or other gait, task and/or physiological characteristics).
This feature of automatic self-reporting represents an improvement
in accuracy over self-reporting of near fall instances by the
person. Self-reporting can be highly unreliable because it is
subjective in nature, relying on the patient's memory and
motivation, and/or lacks sensitivity, in that a patient might not
recognize that an experience was in fact a near fall (particularly
if its magnitude was low). Self-reporting also usually requires a
long observation period, such as six months or a year. Optionally,
the systems described herein, while usable for long periods, can be
used for short periods, such as 1-10 hours, 1-10 days or 1-10
weeks, or intermediate periods.
[0119] As will be discussed in greater detail below, near fall and
actual fall detection in some embodiments of the invention is
measured based on acceleration of person 22. Some embodiments are
based on the inventive realization that whereas regular walking is
a controlled form of movement that involves a consistent level of
acceleration, when there is a fall there is a loss of control
resulting in a much higher level of acceleration. Movement data
obtained for near falls and other parameters can be used to
construct a "gait acceleration profile" that is particularly
configured to the movement or gait characteristics of the person.
It is hypothesized, without being limited to any particular
hypothesis, that one or more parameters of the person's gait
acceleration profile constitute a useful measure or indicator of
loss of control by person 22. Alternatively, one or more parameters
of the gait acceleration profile may be viewed as an indicator of
over-control by person 22, since in recovering from a near fall and
avoiding a real fall, person 22 has made a successful attempt to
regain control.
[0120] In some embodiments of the invention, detector 20 counts
both near falls and actual falls. Data relating to both experiences
comprise the gait acceleration profile of the person. The two types
of events may be lumped together, or alternatively, upon further
analysis of the data, instances of near falls may be separated from
instances of actual falls. Optionally, falls are detected based on
the sudden deceleration at the end of a fall, or based on the time
of the fall and/or a time integral of velocity or acceleration
which indicates vertical distance moved of the sensors.
[0121] In some exemplary embodiments of the invention, detector 20
is configured to detect gait irregularity. Optionally, detector 20
is configured to detect gait irregularity in addition to near fall
detection. Optionally or alternatively, detector 20, or a variation
thereof, is configured to detect gait irregularity irrespective or
instead of near fall.
[0122] In some embodiments, gait irregularity detection is based on
vertical acceleration. Typically, corresponding to gait's steps,
the acceleration signal exhibits a generally cyclic pattern with
characteristic peaks. In some embodiments, irregularity is
determined when the periods of the cycles (e.g. between peaks) vary
above a threshold. Optionally or additionally, the irregularity is
determined when the shape of the cycles vary above a threshold,
where the variability of the shape is determined, for example, by
cross-correlation. Optionally or additionally, the irregularity is
determined by a frequency spread of the acceleration signal,
obtained for example, with a Fourier transform.
[0123] In some embodiments, detector 20 is configured to assist in
regulating a person's gait. Responsive to a detected gait
irregularity of a person, the person is prompted by cuing signals,
such as audio message or vibration, to adjust and/or stabilize the
gait.
[0124] In some embodiments, detector 20 is configured to enhance a
Timed Up and Go (TUG) test to assess the tendency of a person to
fall. In some embodiments, the enhancement is based on time
derivative of the vertical acceleration. In some embodiments, a
tendency to falling is detected when the rate of change of the
acceleration is above a threshold. In some embodiments, the
threshold is based on the rate of change of acceleration of healthy
person or persons. Optionally or additionally, the threshold is
based on the physiological state of the person being assessed, such
as neurological disorder.
2. Exemplary Structure
[0125] FIGS. 2A, 2B, and 2C shows the component elements of three
exemplary embodiments of near fall detector 20.
[0126] The embodiment of FIG. 2A is a self-contained device, in
which all of the elements are contained in a common housing or
casing 30. As discussed in greater detail below, this embodiment
includes features that provide real-time feedback to the user.
Accordingly, this embodiment could be used as near fall detector 20
in the example of FIG. 1.
[0127] As shown in FIG. 2A, near fall detector 20 includes a sensor
32. This component may be any sensor configured to measure an
aspect of movement such as a change of acceleration, velocity, or
position. An accelerometer, which is a type of sensor that measures
acceleration directly relative to freefall, may be used for sensor
32 in some embodiments. Accelerometers are convenient to use
because they are widely available and inexpensive relative to
specialized acceleration measuring devices. In addition, as will be
discussed in greater detail below, measuring acceleration directly
provides the benefit of reducing the processing burden on the
device, as compared with a sensor that measures position or
velocity. The tolerance or sensitivity of sensor 32 should be about
800 mV/g or better. The sampling frequency of sensor 32 may be
about 100 Hz, and optionally is not less than about 60 Hz in order
to obtain adequate results. A sampling rate that is too low may
adversely affect sensing quality.
[0128] A parameter of sensor 32 is the number of axes in space in
which the sensor takes its measurements. Tri-axial sensors 32 are
configured to measure in all three orthogonal orientations in
space, specifically the vertical, medio-lateral, and
anterior-posterior directions. A single axis sensor may measure
along one axis only, such as in the vertical direction, and a
bi-axial sensor measures in two directions. Sensor 32 of the
present invention may be a tri-axial sensor in all embodiments, but
may also be a bi-axial or single axis sensor in some embodiments,
as long as one of the axes of measurement is the vertical axis. In
some cases a bi-axial or single axis sensor may be less expensive
than a tri-axial sensor. However, the use of tri-axial sensors may
enhance detection accuracy and reliability, and may also provide
the monitoring physician with additional information about the
direction and nature of any near falls. An example of an
accelerometer that may be used for sensor 32 is the Dynaport,
manufactured by the McRoberts company of the Netherlands. If a
single axis accelerometer is used, detector 20 optionally includes
an indicator (e.g., an arrow) to show which part of detector should
be aimed in a certain direction (e.g., up).
[0129] In this embodiment sensor 32 transmits the measured movement
data to a processor 34. As shown, the transmission is made through
a sensor output port 31 on sensor 32, which connects directly to a
processor input port 33 of processor 34.
[0130] Processor 34 may be a numeric processor, computer, or
related electronic component such as an application specific
integrated circuit (ASIC), electronic circuit, micro-controller, or
microprocessor capable of processing the raw movement data measured
by sensor 32. Optionally, the speed of processing, such as a speed
of a computation cycle of measurement or measurements of sensor 32,
is at least that of the sampling frequency of sensor 32. In some
embodiments processor 34 records acceleration values and calculates
derivatives or other parameters of acceleration. Processor 34
further includes and/or is coupled with software (not shown) that
directs operation of the processor. Internal memory (not shown) may
optionally be included in and/or is coupled with processor 34 to
store logged and derived acceleration values, and/or other
numerical values calculated by the software. Alternatively or
additionally, processor 34 may connect with a separate memory
module 36 to store these values. In some embodiments, processor 34
is further configured to control some or all aspects of a user
interface 38 and/or a radio transmitter or receiver or combined
transmitter/receiver ("transceiver") 40. Connection with these
elements may be made in some embodiments through a processor output
port 42 and a user interface input port 43.
[0131] Processor 34 may also connect with an external device such
as a computer through an optional external interface port 44. This
connection may enable processor 34 to transfer data to the external
computer and/or to receive a software program, software updates, or
other inputs, for example, by a physical connection (e.g. wired)
and/or wirelessly such as using a Bluetooth or a Wifi or cellular
connection. In some embodiments, external port 44 may be a USB port
or other industry standard connection. For additional flexibility,
external port 44 may comprise two or more such ports rather than
just one.
[0132] After person 22 has used the device for a given period of
time, a record of the person's near fall and other gait related
data is optionally stored in the device (optionally as it occurs).
This processed data may be provided to the person's doctor by
connecting device 20 through port 44 into a corresponding port,
such as a USB port, of a computer. The data may then be transferred
between devices in the manner well known in the art. In practice,
person 22 may hand device 20 to the doctor or doctor's staff when
visiting the doctor for an appointment, and the information may
then be transferred to the doctor's computer directly.
Alternatively, person 22 might transfer the information to his or
her own computer and then email it to the doctor. Alternatively,
the information might be sent wirelessly directly or indirectly
from device 20 to the doctor's computer or another location, for
example, by email. Another embodiment includes real-time transfer
of the data as it is processed for online monitoring. In some
embodiments, the memory is or comprises a removable card such as an
SD card. Data on the card can be read by a card reader, and the
data is optionally transferred to a computer and/or for archiving
such as on hard disk or CD or DVD.
[0133] User interface 38 is an element of near fall detector 20
configured, in some embodiments, to provide information to the user
or person 22 and/or to receive information from the user. The
information may be in any convenient format such as visual, audio,
and/or touch, and may be configured to meet the particular needs of
the user. For example, in some embodiments user interface 38 may
emphasize audio-based elements rather than visual elements, to
better meet the needs of elderly users whose sight is weak.
[0134] User interface 38 may optionally include information output
elements such as a visual display screen 46 capable of displaying
alphanumeric and/or graphical messages, a speaker 48, and/or alarm
lights 50. Optional user input elements include a touchscreen 52,
microphone 54, keypad, and touchpad (not shown). In some
embodiments, user interface 38 may include a camera and/or a video
recorder.
[0135] In some embodiments, visual display screen 46 may also
include the functionality of touchscreen 52, and accordingly
comprise a means for both displaying information to the user and
receiving information from the user. Visual display screens 46
based on liquid crystal technology (LCD) may be used due to their
readability and low power requirements, but other types of display
and/or touchscreen technologies may also be used.
[0136] As noted, near fall detector 20 optionally includes wireless
transceiver 40. In a handheld device, transceiver 40 in some
embodiments will operate at relatively high frequencies such as
from about 100 MHz to 2 GHz, this may allow a device to be made
smaller. Transceiver 40 optionally connects to processor 34 through
processor output port 42, and may include a transponder (not
shown), antenna 41, and other radio frequency components required
to maintain wireless communication. In some embodiments transceiver
40 may comprise a radio and antenna such as that used in a cellular
telephone or, in other embodiments, components of the type used in
a computer standard Bluetooth interface.
[0137] In order to power the elements of near fall detector 20, an
energy source such as a battery 56 may be used. In some embodiments
battery 56 is a light weight battery that provides power for an
extended number of hours, or even several days or weeks. In this
way, near fall detector may be used for the greater part of a day,
and enable a meaningful amount of data to be gathered. In some
embodiments battery 56 is a lithium ion battery, but other battery
types, for example, rechargeable or one-time may be used as
well.
[0138] The various optional elements of user interface 38, along
with transceiver 40, may be combined to provide a range of
responses that assist person 22 in the event of a near fall or a
fall. For example, upon detecting a near fall or fall, speaker 48
could emit an audible beep and then deliver a message in the form
of a human voice asking if the person is ok, and requesting person
22 to press a button on the device or screen for confirmation.
Alternatively, the message could be a visual one on display screen
46. If the user signals that he or she is ok no further action need
be taken. If the user suggests otherwise or does not respond within
a predetermined time, near fall detector 20 may be programmed to
automatically send an email, page, or text message to a family
member or doctor to alert them that person 22 fell or has almost
fallen and needs assistance. An optional geographical position
system (gps) in near fall detector 20 may automatically inform the
doctor of the location of the person. In some embodiments, the
device could automatically dial the doctor's phone number to enable
direct voice communication.
[0139] In some embodiments, near fall detector 20 could be
programmed to engage person 22 in a dialogue, to obtain more
precise information. Person 22 could respond in a variety of ways,
such as by keyboard, touchscreen, or by speaking into microphone
54. Sample questions from such a dialogue may be, for example, "did
you fall?", "are you ok?", "where are you?", "do you need help?",
and "would you like to call your doctor/spouse?". The device might
also be used to record a voice or video message by person 22 and
forward the message to the assisting party.
[0140] Housing or casing 30 is optionally sized and/or shaped
sufficiently large to enclose the various components. Internal
elements such as sensor 32 and processor 34 are optionally shielded
from the elements, and/or user interface elements such as a
keyboard, visual display screen 46, if present, are optionally easy
to access. Housing 30 is optionally made of a rigid and durable
plastic, but other materials that are light and strong, such as
aluminum, may also be used. Optionally, housing 30 includes a clip
(not shown) for convenient attachment to belt 24 or other article
of clothing. If sensor 32 requires a particular orientation when
the device is mounted on belt 24 in order to operate effectively,
visual or audio feedback may be provided by the appropriate
elements of user interface 38 to assist person 22.
[0141] Near fall detector 20 in some embodiments of the invention
may comprise a dedicated device having as its only or primary
function the detection of near falls and actual falls. In some
embodiments, near fall detector 20 may be incorporated into other
types of electronic devices used primarily for other purposes
unrelated to fall detection. Examples of such devices include
cellphones, pagers, portable media players, mobile Internet
devices, and the like. This configuration may be more convenient
for the user as it reduces the number of devices to be carried, and
may also reduce the risk that the user will forget to take near
fall detector 20.
[0142] In some of these embodiments all or most of the hardware
elements may already be available as part of the function of the
device. For example, some cellphones known as "smartphones" and
even some "regular" cellular telephones and PDAs include relatively
powerful computer processors, accelerometers, visual display
screens and speakers, wireless telephone and data communication
hardware, and the like. Accordingly, some smartphones may only
require the addition of specialized software to become configured
as near fall detector 20, according to some embodiments of the
invention. In some instances the smartphones may need other
modification such as the addition of memory module 36 and/or adding
of a sensors, optionally with wired or wireless linking to the
smartphone.
[0143] In some embodiments of the invention, near fall detector 20
may be incorporated into a medical device implanted in (or carried
by) the user's body for medical purposes, such as a brain pacemaker
for example. Other examples of such implanted devices include heart
pacemakers, prosthetic hips, and implanted pumps for chronic pain.
Similar to smartphones, some of these devices may already include a
processor or accelerometer and accordingly may only require
software to function as near fall detector 20, according to some
embodiments of the invention.
[0144] Turning now to FIG. 2B, in this embodiment processor 34 is
separated from the portable part of device 20 contained in housing
30 and placed at a remote location. In an exemplary embodiment of
the invention, remote processor 34 receives movement data from
sensor 32 in real time (e.g. sufficiently fast to detect a near
fall) through transceiver 40, and communicates with and controls
user interface 38 through wireless communication. Remote processor
34 otherwise functions similarly to integrated processor 34 of the
embodiment of FIG. 2A, in that it processes and monitors near falls
and communicates with person 22 and doctors or other assisting
parties. Since this embodiment performs data analysis in real time,
it could be used as near fall detector 20 in the example of FIG.
1.
[0145] In this embodiment, a local processor 35 may be included in
housing 30, for example, primarily to manage operation of the
portable device 20. Local processor 35 may accordingly be
relatively less powerful than remote processor 34 (e.g. lower
requires less power). In some embodiments local processor 35 may
perform a portion of the data processing to ease the burden on
remote processor 34 and/or reduce transmission volume e.g. to
reduce power and/or required bandwidth. In this embodiment
processor 34 may be stationary and placed at a fixed location
within the range of transmission of mobile device 20. Additionally,
memory module 36 may also be remotely located and connected to
processor 34. Processor 34 in this embodiment is conveniently a
general purpose computer such as a personal computer rather than an
electronic component such as an ASIC or microprocessor, and memory
module 36 may be the hard disk drive of computer 34.
[0146] The distance at which mobile device 20 may travel from
stationary remote processor 34 will vary depending on the type of
wireless technology used by transceiver 40 and the power available
in battery 56. In some embodiments the wireless technology may be
Bluetooth, which has a range of several meters. In some embodiments
cellular telephone technology may be used, which has a much larger
range, potentially in the kilometers. However, as the distance
increases the potential for disruption in communication that would
adversely affect real time feedback increases. Accordingly, this
embodiment may be particularly suitable in a closed environment in
which a multiple number of persons need to be monitored, such as a
nursing home or a hospital. The aspect of multiple patients each
having a mobile device 20 and sharing remote processor 34 is
represented in FIG. 2B by multiple dashed rectangles 20.
[0147] FIG. 2C shows another embodiment of near fall detector 20.
This embodiment is similar to the embodiment of FIG. 2B in that
processor 34 is remote and housing 30 includes local processor 35.
However, in this embodiment there is no transceiver 40 or wireless
communication between mobile device 20 and remote processor 34, and
memory module 36 is connected to local processor 35 inside mobile
device 20. In operation, mobile device 20 accumulates near fall
data and stores the data in memory module 36 for later offline
processing by remote processor 34. The data may be transferred to
remote processor 34 through external interface port 44 in the
manner described previously. In this embodiment user interface 38
is optional. In some embodiments there is no user interface 38
other than in some embodiments, on/off switch. In other embodiments
user interface 38 may be a single element such as display screen
46, to guide the user in setting up the device. If this embodiment
does not provide real time analysis and feedback, it is optionally
not used as near fall detector 20 in the example of FIG. 1.
[0148] In some embodiments, detector 20 is configured to detect
gait irregularity based acceleration measurement. In some
embodiments, detector 20 is further configured to generate cuing
signals responsive to detection of a gait irregularity. For
example, using speaker 48 to sound messages such as `step . . .
step . . . `, and/or generate audible `ticks` akin to a metronome,
or any sound to indicate a regular pace. As another example, a
vibrator is attached to the person arm and/or or optionally
comprised in detector 20, and vibrations are generated to indicate
a regular pace. In some embodiments, other methods are used to
indicate or prompt a regular pace, such as sending an audible
prompt to a earphone or hearing aid by a Bluetooth connection or a
wire connection.
[0149] Optionally or alternatively, detector 20 is configured to
assist in detecting tendency to fall in during a Timed Up and Go
(TUG). For example, detecting rate of change of acceleration during
sitting or rising movements and determining if the person is prone
to fall according to threshold criterion of the rate of change. In
some embodiments, the determined tendency to fall (and/or lack
thereof) is reported on display screen 46. Optionally, rate of
change and, optimally, the criterion that was used is reported on
display screen 46. In some embodiments, the rates of change and
criterion used are stored in detector 20 for further study. In some
embodiments, the rates of change and criterion used are transferred
to other devices as described above.
[0150] In some embodiments, determination of gait irregularity
and/or tendency to fall is based on the measurement or an
accelerometer such as sensor 32. Optionally or alternatively,
additional or different accelerometers or sensors are used.
[0151] In some embodiments, configuring detector 20 is carried out
by modifying the software program and/or electronic circuitry (e.g.
re-programming an FPGA). Optionally, in case sensors other than
senor 32 are used, the program and/or electronic circuitry are
adapted to the other sensors. In some embodiments, in configuring
detector 20, processor 34 may be changed and/or an additional
processor is incorporated in detector 20.
[0152] Referring to detector 20 implies, without limiting, also
variations thereof or similar devices that use one or more
accelerometers.
3. Exemplary Operation
[0153] FIGS. 3A and 3B are flow charts that illustrate exemplary
operation of near fall detector 20, according to an embodiment of
the invention. FIG. 3A provides a broad overview and FIG. 3B
provides a more detailed view of the method of gait data collection
of the invention. The modules shown in the flow charts represent or
correspond to processes and methods that can be carried out in
software and executed by processor 34.
[0154] In these figures, the illustrated processes are based on an
embodiment of near fall detector 20 that uses an accelerometer or
other sensor 32 that measures acceleration directly or other
movement parameters such as angular velocity or tilt. Embodiments
of the invention that use sensors that measure different aspects of
movement, such as velocity or position, may include extra steps
that involve taking derivatives of velocity and/or position in
order to obtain an estimate of acceleration and/or may measure
movement parameters other than acceleration. It may be advantageous
to use an acceleration based sensor 32 in some embodiments, since
it is more accurate and enables processing with fewer steps and
accordingly provides a faster overall processing time.
[0155] In exemplary embodiments of the invention, beginning with
FIG. 3A, upon starting and calibrating the device, sensor 32 begins
to measure acceleration for the current gait segment of time
T.sub.n (module 110). The gait segment T.sub.n is simply the
inverse of the sampling frequency, e.g. 0.01 seconds for a sampling
frequency of 100 Hz. Acceleration is measured in the axes for which
sensor 32 is configured, i.e. vertical, medio-lateral, and
anterior-posterior for a tri-axial sensor.
[0156] In some embodiments, the raw acceleration data is then
passed to processor 34 (module 120), through sensor output port 31
and processor input port 33. Processor 34 performs one or more
calculations to obtain certain parameters that are used to obtain a
gait acceleration profile of person 22. These parameters may be
called "derived parameters" since they are derived from the raw
movement data provided by sensor 32. Processor 34 optionally
calculates a dynamic threshold for each derived parameter. The
threshold is optionally "dynamic" because it is based on and
updated from the stream of acceleration values received for each
period T.sub.n.
[0157] Upon calculation of these values, processor 34 optionally
determines whether a near fall has occurred (module 130). In making
this decision, processor 34 compares each derived parameter with an
associated threshold value. The threshold value may be the dynamic
threshold calculated earlier, or a predetermined "static"
threshold. A near fall is indicated if a particular parameter
exceeds its threshold. In addition to comparing individual derived
parameters with their threshold, processor 34 may optionally also
combine any two or more individual parameter results using logical
operators such as OR and AND.
[0158] In some embodiments, upon completing a plurality of
comparisons, processor 34 will make an overall determination of
whether a near fall has occurred. If every comparison indicates a
near fall (or optionally a subset such as a majority the number of
comparisons indicate a near fall), then the determination of
decision module 130 will be "Yes", a near fall has occurred. If
none of the comparisons indicate a near fall, the determination
will be "No", a near fall has not occurred. In most cases the
results lie somewhere in between, with some comparisons indicating
a near fall and some indicating no near fall. Processor 34, in some
embodiments of the invention, may be programmed to assign a
likelihood of a near fall according to a predetermined sensitivity
set by the doctor in accordance with the particular medical profile
and fall risk of the patient. For example, the physician may set
near fall detector 20 to determine that a near fall has occurred if
half or more of the comparisons indicate a near fall, and to
determine no near fall otherwise.
[0159] As indicated in the flow chart of FIG. 3A, if it is
determined that a near fall has not occurred the system moves on to
the next time period T.sub.n, for n=n+1 (module 140), and the
process is repeated with a new sensor measurement (module 110).
However, if it is determined in module 130 that a near fall has
occurred, near fall detector 20 may then respond in some manner
(module 160). As described earlier, the response could, for
example, take the form of any one or combination of logging a
record of the near fall event, prompting the user by display or
audio, querying the user to obtain more information, and/or
communicating with another party for assistance.
[0160] Decision module 170 asks whether near fall monitoring should
continue. This will depend on the seriousness of the near fall. If
the near fall was a relatively minor event that did not overly
stress the user then control passes to module 140, "n" is
incremented, and the process repeats at module 110. Otherwise near
fall monitoring may stop (module 180) as the user recovers from the
effects of the near fall or fall. Optionally, the stop is for a
limited period of time and/or until a rest is performed.
[0161] Turning now to the flow chart of FIG. 3B, the processes
performed by near fall detector 20 may now be reviewed in greater
detail. Again, upon startup and calibration (module 100), sensor 32
measures acceleration for the current gait segment of time T.sub.n
(module 110). In module 120, as noted, processor 34 calculates
derived parameters of acceleration (and/or other movement
signals).
[0162] The derived parameters in some embodiments may include, for
example, any one or combination of the following six example
parameter types:
[0163] 1) "Max" is the maximum measured acceleration value. For
example, a measurement of acceleration along the vertical ("y")
axis that is the maximum such value for a period of time may be
referred to as "Vertical Max".
[0164] 2) "Maxp2p" is the maximum peak-to-peak value (positive peak
to negative peak within a single cycle) of the measured
acceleration over a period of time.
[0165] 3) "SVM" is the signal vector magnitude. This is calculated
as the square root of the sum of the squares of the measured
acceleration, for each axis measured. For example, using a
tri-axial sensor 32, SVM is the square root of the sum of
(x.sup.2+y.sup.2+z.sup.2), where x, y, and z are the measured
acceleration values in the medio-lateral ("x"), vertical ("y"), and
anterior-posterior ("z") directions.
[0166] 4) "SMA" is the normalized signal magnitude area. This is
calculated as the sum of the absolute values of the acceleration
along each measured axis, integrated over time "t". The sum is
divided by "t" to obtain the normalized value.
[0167] 5) "Maxdiff" is the maximum acceleration derivative. This is
obtained by taking the derivative of the measured acceleration
(sometimes called the "jolt"), and is the maximum of this
value.
[0168] 6) "Maxp2pdiff" is the maximum peak-to-peak acceleration
derivative. Like Maxdiff this is also based on the acceleration
derivative or jolt rather than the raw acceleration value. This
parameter is the maximum value between positive peak and negative
peak of the acceleration derivative within a single cycle over a
period of time.
[0169] The inventors have observed that use of the above six
parameters, and even a small subset of the six including as few as
one or two parameters, have provided adequate results in some
embodiments. In some embodiments, additional derived parameters
other than the six described above may also be calculated by
processor 34 and used to determine near falls, optionally in a more
robust manner.
[0170] In some embodiments, the "Vertical Max" parameter is
included, solely and/or in combination with other parameters, in
the determination of a near fall.
[0171] Returning to the flow chart of FIG. 3B, in module 120
processor 34 calculates or updates an incremental value for a
particular derived parameter. In module 122, processor 34 updates a
dynamic threshold value for this parameter. In module 124 the
system queries whether there are any other derived parameters to be
calculated. If the answer is "Yes" control is returned to module
120 and the process repeats. Accordingly, if for example the system
is programmed to use three derived parameters, then modules 120 to
124 will loop three times before proceeding to module 130.
Alternatively, a flow process in which processor 34 calculates all
of the derived parameters first, and then calculates all of the
associated thresholds is also comprehended by the present
invention. In embodiments that use a static threshold instead of a
dynamic threshold, module 122 may be bypassed or its results
ignored. In embodiments that use only one derived parameter,
decision module 124 may be bypassed.
[0172] The calculation of dynamic threshold for each derived
parameter in module 122 may be performed in a variety of ways. In
some embodiments, a mean and standard deviation of the parameter
may be calculated and updated with each successive measurement. The
threshold may then comprise the mean value plus some multiple of
the standard deviation. For example, a "usual-walk" period of time
may be identified, based perhaps on measures of rhythmicity and
regularity, and one or more derived parameters and their mean and
standard deviations estimated based on this usual-walk episode. If
in any subsequent window of time the value of one of these derived
parameters exceeds the mean plus three times the standard deviation
of that observed during the usual-walk, the algorithm will record
this parameter as detecting a near fall. For other activities, such
as stair climbing (e.g., similarly identified from the gait
signals, or based on displacement as a function of time), other
thresholds may be applied. Optionally, a user can indicate, for
example, during a calibration stage, if a recent event was a near
fall or not. This may be, for example, initiated by the user, or by
the system asking regarding a specific event.
[0173] Unlike the dynamic threshold, the calculation of the static
threshold is optionally performed offline, at some time prior to
operation of the near fall detector 20. Parameter data may be
obtained for a time period in which a person's walk is directly
observed (or recorded for later observation). From this, two groups
of time periods or intervals may be defined, comprising "near fall"
groups and "non-near fall" groups. Since the near fall groups have
been directly observed and are known to be accurate, they comprise
a "gold standard" of known near falls that may be correlated with
the signal processing data.
[0174] In some embodiments, the static threshold may be calculated
as an optimization of sensitivity and specificity with respect to a
single or multiple number of derived parameters. The algorithms
used may be non-linear and advanced. Some examples of the types of
discriminant functions that may be employed include linear,
diaglinear, quadratic, diagquadratic, and mahalanobis. Algorithm
performance may then be measured in terms of sensitivity (true
positive/(true positive+false negative)) and specificity (true
negative/(true negative+false positive)).
[0175] In decision module 130 processor 34 determines whether a
near fall has occurred in time period "n" based on the updated
derived parameter values. As noted above, the determination may be
made by subtracting (or comparing in another way) from the derived
parameter value the value of its associated threshold. In
embodiments that use dynamic thresholds, the threshold values
calculated in module 122 are used. In embodiments that use static
thresholds, the threshold values will have been pre-loaded in
memory and may be retrieved at the time of the calculation. Also as
noted, in some embodiments a plurality of such comparisons are made
involving individual parameters and combinations of parameters.
[0176] The inventors have discovered that, in some embodiments,
adequate detection of near falls may be obtained through the
calculation of a single derived parameter, Maxp2pdiff, based on
acceleration along the vertical axis. The inventors observed that
vertical Maxp2pdiff identified near falls with a sensitivity of
85.7% and a specificity of 88.0%. It may be noted that in this
case, decision module 130 would only need to review a single
comparison of Maxp2pdiff with its associated threshold, as no other
comparisons need to be considered.
[0177] The inventors have also discovered that, in some
embodiments, adequate detection of near falls may be obtained
through the calculation of two derived parameters, Maxp2pdiff and
Max, both based on acceleration along the vertical axis, and by
performing a logical AND operation on the individual results.
Accordingly, this method will find a near fall only in the event
that both parameters exceed their respective thresholds. The
inventors observed that this method of detection identified near
falls with a sensitivity of 85.7% and a specificity of 90.1%.
[0178] An illustration of the results using the above methods of
detection is shown in FIG. 4. As indicated, in the time period
recorded in the graphs, person 22 had three near falls or missteps.
In FIG. 4, the lower graph shows Maxp2pdiff and the upper graph
shows Vertical Max over this time period. It may be seen that at or
about the time of each misstep, both derived parameters display
distinct increases in value relative to their average values over
the balance of the time period. Accordingly, a gait acceleration
profile based on the derived parameter Maxp2pdiff, or one based on
the logical combination of Maxp2pdiff "AND" Vertical Max, may be
used to detect near falls with adequate results.
[0179] As noted, the present invention comprehends many other
selections of specific derived parameters and combinations of
derived parameters to determine near falls. In another example, all
six derived parameter examples may be calculated, and near falls
could be determined if any three or more confirm a near fall.
Through logical combinations of individual parameters many more
comparisons may be made and considered in determining a near fall
to enhance robustness of the decision tree. The various comparisons
could be listed in a hierarchy and determination of a near fall
could be made along a gradient that corresponds with the results of
the plurality of comparisons. The output could be binary (i.e.
"yes/no" a near fall has likely occurred) and/or a continuous
measure related to the likelihood that a near fall has occurred,
based on the number of parameters exceeding thresholds. For
example, an embodiment may have 100 comparisons involving the
different parameters individually and in various logical
combinations. The comparisons could represent 100 "levels" over
which the near fall is graded, ranging from a sure near fall at one
end to a sure non-near fall at the other end. Similarly, output
scores could be graded based on the percent of steps in which a
misstep occurred.
[0180] Optionally (e.g., as discussed above), the determination of
a person's near fall experience may be used to prepare or modify
that person's gait acceleration profile. Near fall detector 20 may
also be used in some embodiments to determine other aspects of a
person's gait that enhance the gait acceleration profile. For
example, if a near fall has occurred, it is useful to know the
magnitude and direction of the near fall. It is also useful to know
(e.g. detect or note) if the incident has resulted in an actual
fall. Other useful gait parameters arise from a study of the
person's walking motion, and include, for example one or more of,
step width, step or stride regularity, and symmetry between
steps.
[0181] The calculation of gait parameters that arise from the near
fall may be seen in flow chart of FIG. 3B in the series of modules
that follow a "Yes" determination of module 130.
[0182] In module 142, the magnitude of the near fall is optionally
determined. Magnitude may be obtained from the peak of the
acceleration, i.e. the derived parameter Max. Alternatively, in
some embodiments that calculate the derived parameters SVM and/or
SMA, these parameters may be used individually or in combination to
obtain a better quality of the magnitude of the near fall. A
magnitude value derived from SVM and/or SMA is considered to be
more robust and stronger than a value derived from acceleration
data alone. The magnitude value may be converted and presented on a
number on a scale, for example between 1 and 100. In reviewing a
person's gait acceleration profile, it is helpful to know that the
person's near falls had an average magnitude of 70, for example, as
opposed to an average magnitude of 20.
[0183] In module 144, the direction of the near fall is optionally
determined. This parameter can enhance the ability to extract
meaning, at least as an estimation, and interpret the gait
acceleration profile by providing the direction of a near fall
relative to vectors along the vertical, medio-lateral, and
anterior-posterior axes. It may be noted that in order to obtain
directional near fall information sensor 32 is optionally
configured to obtain measurements along all three axes.
[0184] The directional information provided by this parameter may
be useful in aiding diagnosis by a physician. For example, falls
that occur to the side are more likely to result in a broken hip,
which are particularly troublesome and dangerous to elderly
persons. Accordingly, the awareness of such data may trigger
preventive action that could prevent a disabling fall that might
otherwise occur. In another example, a persistent trend to near
falls in a particular direction might indicate a structural
weakness or postural problem, which may lead to preventive
physiotherapy, adoption of a walking aid, or wearing asymmetrical
protection such as a pad on one hip.
[0185] Optional modules 146 and 148 optionally provide information
that assists in determining if a real fall has occurred. After a
real fall, there is often a silent period since the person is not
moving. Accordingly, module 146 collects sensor information for a
period T.sub.x after the near fall. If the measured values are zero
or close to zero (or reflect a vertical location that is near the
floor and/or a small range of motion (e.g., by integrating
acceleration over time)), it would suggest that a real fall has
occurred. Module 148 estimates the height or position of the
person's center of mass after the near fall. The center of mass may
be estimated from a gyroscope, if that instrument is provided in
near fall detector 20. In some embodiments, an accelerometer such
as that used for sensor 32 may be used to estimate the height of
the center of mass.
[0186] Decision module 150 optionally considers the above
information in determining whether a real fall has occurred. This
module may also consider the magnitude value obtained in module
142, since in a real fall the magnitude value tends to be higher
than for a near fall. If a real fall is determined, module 152 logs
data relating to the incident, such as the time and day, magnitude,
and direction. In module 160, near fall detector responds in the
manner described earlier, by interacting with the person and
possibly contacting an outside party. If module 150 determines that
a real fall has not occurred, the event is logged as a near fall
(module 154). An optional module 156 may consider the parameters of
the near fall in deciding whether to respond (module 160), or
whether to proceed to module 140 to increment "n" and repeat the
sequence at module 110.
[0187] Returning to decision module 130, if processor 34 determines
that a near fall has not occurred, gait parameters such as step
width, step or stride regularity, and symmetry between steps may
optionally be determined. These parameters can provide additional
information about the patient's balance and gait that can not be
obtained simply by observational analysis or self-report. These
parameters are also independent of one another, and accordingly
provide complementary, objective data that enhances the quality of
the patient's gait acceleration profile.
[0188] Beginning with optional module 132, the step width parameter
may be determined as the distance in the horizontal or
medio-lateral direction between the subject's feet, orthogonal to
the direction of movement. It may be noted that to calculate step
width sensor 32 is optionally configured to measure along the
medio-lateral axis. It may also be noted that step width is a
distance value. Accordingly, if sensor 32 is an accelerometer that
measures acceleration directly, the measured value would generally
have to be further processed, such as by double integration, to
obtain an estimate of the step width distance.
[0189] The step width parameter may be useful for the gait
acceleration profile of a patient in that if it is found to be
wide, it may be an indication that the patient is over
compensating. A step width that is not consistent and is too
variable is considered to be unhealthy, and accordingly may prompt
further diagnostic testing by the doctor.
[0190] Optional module 134 may be used to calculate step or stride
regularity. This parameter is a measure of the repeatability,
regularity, or consistency of the person's gait, and can refer to
the length or the timing of the step. Useful information may be
obtained along a single axis or from all three axes. This parameter
is typically calculated by an autocorrelation of the raw
acceleration data.
[0191] A stride of walking is the time to complete one walking
cycle, for example from the left foot touching the ground to the
subsequent instance of the left foot touching the ground. One
stride equals two steps. Accordingly the terms "step regularity"
and "stride regularity" mean essentially the same thing, with the
only difference being the portion of the gait cycle over which they
are measured.
[0192] Measures of regularity can be used to define the degree to
which the person's walking pattern is rhythmic. In medical terms,
the greater the regularity and "rhythmicity", the healthier the
motor control system is considered to be in the patient.
[0193] Optional module 136 may be used to calculate symmetry
between steps. This parameter measures the degree of equality
between steps taken by the left foot relative to steps taken with
the right foot. It may be calculated by the formula:
Gait Asymmetry=100.times.|ln(SSWT/LSWT)|.
[0194] In the formula, "SSWT" and "LSWT" stand for the mean values
of the Short and Long Swing Time, respectively, as determined from
the vertical axis.
[0195] Other measures of asymmetry, such as one based on step
times, for example, may also be used in some embodiments to provide
a more complete estimate of asymmetry patterns.
[0196] For example, identifying cycles periods in accelerometer
signal or signals (e.g. peak to peak) and determining the
variability (the irregularity) of the cycles' periods.
[0197] In some embodiments, an asymmetry measure such as difference
between the longest and shortest cycles may be used. Optionally or
alternatively, the standard deviation of the cycles' periods may be
used. Optionally or alternatively, some other statistics such as
the median of the period may be used.
[0198] In some embodiments, a measure of regularity of asymmetry
may be obtained in a frequency domain, optionally within locomotion
band (stride) such as 0.5-3.0 Hz. A narrow frequency spread (e.g.
standard deviation) indicates regular stride and, vice versa, wide
spread indicates irregularity and possibly a sign of physiological
or neurological disorder.
[0199] FIG. 5 shows exemplary charts of stride acceleration and
frequency spread of a healthy person and a person with Parkinson
disease, respectively.
[0200] Charts 501 and 503 are of a healthy and Parkinson diseased
persons, respectively, illustrating the acceleration in the
anterior-posterior direction, and charts 502 and 504 illustrate the
respective frequency range. Vertical axis of charts 501 and 503 is
acceleration (in g) and the horizontal axis is in seconds;
horizontal axis of charts 502 and 504 is in Hertz and the vertical
axis is the frequency amplitude.
[0201] The sharper and narrower peak of chart 502 with respect to
chart 504 reflects a more consistent gait pattern, i.e., reduced
gait variability and lower stride-to-stride fluctuations of a
healthy person relative to a Parkinson diseased person.
[0202] In some embodiments, a measure of stride regularity or
asymmetry is determined by combining (e.g. averaging) two or more
of the methods described above. Optionally, the combination assigns
different weights to the various measures obtained by the methods
described above. In some embodiments, measures that indicate larger
asymmetry are assigned larger weights relative to measures that
indicate smaller asymmetry.
[0203] Upon completion of the calculation of the various gait
parameters, module 140 increments "n" and the process is repeated
with a new sensor measurement for time period T.sub.n in module
110.
[0204] A further aspect of operation of some embodiments of near
fall detector 20 concerns calibration of the device. Calibration
initializes the device so that the sensor recognizes and accurately
responds to movement along the appropriate axes. In this way, near
falls and other gait parameters can more accurately be measured.
Calibration is helped by measuring along all three axes, as this
enables the device to find the direction of gravity and to orient
itself to align with it.
[0205] In an exemplary embodiment of the invention, calibration
involves performing procedures recommended or instructed by the
sensor or accelerometer manufacturer. In some embodiments of the
invention, such as for example where near fall detector 20 is a
dedicated device worn on the person's belt, the orientation of the
device in space is relatively fixed. Accordingly, calibration in
these cases may be a relatively simple matter. In other embodiments
of the invention, such as when near fall detector 20 is
incorporated in another device such as a cell phone, the
orientation of the device in space is not fixed and will vary
widely in the course of daily use. For example, a cell phone may be
vertical when in use by a standing person, but may be horizontal if
the person is lying down. Further, when put in a coat pocket or
carrying bag the cell phone may be upside down or adopt any other
orientation at random. In these cases the device may self-calibrate
to ensure that near fall detector 20 works properly.
[0206] In some embodiments of the invention, calibration and
operation of the device may be independent of the weight of the
person whose movement is being monitored. For example, near fall
detector 20 will be calibrated and operate in the same manner
whether the user is a heavier person or a lighter person.
4. Exemplary Applications of Gait Acceleration Profile
[0207] As discussed, the gait acceleration profile of a person
comprises that person's observed or recorded gait parameters over
one or more periods of time. For example, a sample gait
acceleration profile of a particular person might be: patient
experienced three near falls over a two day period. The near falls
had magnitudes of 60, 23, and 47 (arbitrary units) and were
primarily in the medio-lateral/left direction. During this period,
step width was 0.31 meters, stride variability (inversely related
to regularity) was 6%, and gait asymmetry was 17. After an
intervention consisting of physiotherapy and prescribed medication,
in an evaluation over a similar two day period, near falls dropped
to one with a magnitude of 14. Step width narrowed to 0.26 meters
and gait asymmetry also improved by a reduction to a value of
11.
[0208] Some embodiments of the invention may enable the benefits of
a detailed patient gait acceleration profile to become available at
greater convenience to both doctors and their patients. An example
of this may be in the area of remote exercise monitoring. There is
a growing push in the medical field for at-home interventions to
improve mobility. A doctor may encourage an older adult or patient
with Parkinson's disease to walk for thirty minutes, five times a
week, with three sessions outside and two sessions indoors on a
treadmill, the latter perhaps having more complex instructions.
Near fall detector 20 in some embodiments may be used for real-time
monitoring as the patient carries out the prescribed exercises. If
a near fall occurs, an alarm can sound or assistance provided
immediately. In this way the safety and usability of such
"tele-rehabilitation" approaches are improved, while at the same
time enabling patient progress to be closely and precisely
monitored. Alternatively, the near fall detector can be used to
assess the efficacy of the prescribed therapy.
[0209] In some embodiments, detector 20, or a variation thereof,
may be used or adapted (e.g. by software and/or circuitry
modification) to enhance common screening of subject prone to falls
or to near-falls, as described below.
[0210] The Timed Up and Go (TUG) test is a widely used clinical
test of fall risk. Subjects are asked to start in a seated
position, stand up and walk 3 meters, turn around, and return to
the seated position. In older adults and other populations such as
patients with Parkinson's disease (PD) or stroke, longer TUG times
have been associated with impaired mobility and an increased fall
risk (for example, Balash Y, Peretz C, Leibovich G et al. Falls in
outpatients with Parkinson's disease: frequency, impact and
identifying factors. J Neurol 2005;252:1310-1315; Najafi B, Aminian
K, Loew F et al. Measurement of stand-sit and sit-stand transitions
using a miniature gyroscope and its application in fall risk
evaluation in the elderly. IEEE Trans Biomed Eng 2002;49:843-851;
Podsiadlo D, Richardson S. The timed "Up & Go": a test of basic
functional mobility for frail elderly persons. J Am Geriatr Soc
1991;39:142-148).
[0211] However, the TUG does not always successfully identify those
with a high fall risk, especially among relatively
well-functioning, healthy older adults (for example, Buatois S,
Gueguen R, Gauchard G C et al. Posturography and risk of recurrent
falls in healthy non-institutionalized persons aged over 65.
Gerontology 2006;52:345-352; Marschollek M, Nemitz G, Gietzelt M et
al. Predicting in-patient falls in a geriatric clinic: a clinical
study combining assessment data and simple sensory gait
measurements. Z Gerontol Geriatr 2009;42:317-321).
[0212] It was observed by the inventors, at least in representative
cases, that extracted accelerometer-based measures such as by
device 20 or similar ones can distinguish or be adapted to
distinguish (e.g. by software adaptation) between elderly fallers
and elderly non-fallers when they perform the TUG, even if TUG
duration times are not significantly different in the two groups.
It was observed that the rate of change of the acceleration during
sitting movement from standing position and during movement of
rising from a seated position is different between healthy persons
and fallers (persons prone to fall, having a tendency to fall).
Healthy persons exhibit a significantly larger rate of change of
the acceleration relative to fallers, at least as observed for
elderly subjects.
[0213] FIG. 6 shows exemplary chart 601 of Timed Up and Go (TUG) of
healthy person and 602 of a person prone to falling (`faller`).
Charts 601 and 602 illustrate anterior-posterior accelerations
measured with an accelerometer, where the horizontal axis is in
seconds and the vertical axis is in -g. The acceleration signals of
charts 601 and 602 are generally divided, respectively, into three
zones, namely, 604 and 614 are when the persons sit from a standing
position, 610 and 612 are walking periods, and 606 and 616 are when
the persons stand from a seated position.
[0214] In the regions of up or down movements 604, 606, 614 and 616
the rate of change of the acceleration was determined as a time
derivative of the measured accelerations (in g/sec), indicated in
FIG. 6 as `jerk`.
[0215] As illustrated in FIG. 6, the rate of change of acceleration
of the jerks of the healthy person and the faller person are
considerably different. The rate of the acceleration of the healthy
person is higher than that of the faller person. For example, as
illustrated, the upward jerk of the healthy person is about 2 g/sec
and the downward jerk (at 606) is about 1 g/sec, wherein the
respective jerks of the faller are about 0.5 g/sec, (at 614 and
616, respectively).
[0216] As detector 20 comprises accelerator and measures
acceleration and rate of change of acceleration, in some
embodiments detector 20 is modified or adapted to distinguish
(screen) fallers from non-fallers based on the amount of the rate
of change of the acceleration in the jerks zones. Thus, in some
embodiments, detector 20 can augment the TUG test by providing
indication for differentiation between healthy persons and persons
prone to fall, at least in some cases.
[0217] In some embodiments, modifying of adapting detector 20
comprises modifying the software program and/or circuitry of the
detector (e.g. different gate array layout). In some embodiments,
the modified or adapted detector 20 provides control (e.g. by
touchscreen or button) to indicate when to measure the jerks.
Optionally or alternatively, the program is adapted to recognize
jerk zones according relative long generally monotonic acceleration
with respect to walking.
[0218] In some embodiments, detector 20 is capable to determine
gait irregularity and asymmetry, as described above. As such,
further to a diagnostic tool, in some embodiments detector 20 can
be used as a therapeutic or an assisting device for regulating the
gait of a subject having a neurological disease or another subject
having a tendency to fall.
[0219] For example, with ongoing assessment of the pattern and
regularity of a gait of a subject, a signal could be automatically
generated responsive detection of deviation from sufficiently
regular or expected gait pattern.
[0220] In some embodiments, the signal indicates that the gait is
irregular or that the subject is about to fall (near fall),
prompting the subject to recover a proper gait.
[0221] Optionally or additionally, the signal indicates suggested
gait pace that the subject can follow in order to stabilize the
gait (cueing signals).
[0222] In some embodiments, the signal indicates suggested pace
irrespective of irregularities, providing continuous training to
the subject, at least for certain time periods. Optionally the
training may, in some cases at least, enhance functional mobility
of the subject.
[0223] In some embodiments, upon detection of a near fall situation
or irregular pace, detector 20 generates an alarm message such as
by speaker 48, notifying the subject of the situation.
[0224] In some embodiments, upon detection of irregular pace,
detector 20 generates audible messages guiding the subject pace,
such as `step . . . step . . . `, thereby assisting the subject to
regulate and stabilize the gait. In some embodiments, the guided
pace is within a determined variability, avoiding too `mechanical`
gait. In some embodiments, the guided pace is adapted and/or
synchronized with the subject's pace.
[0225] In some embodiments, the pace of the cueing signals are
based on behavior detected or assessed in healthy persons,
optionally of about the same age. Optionally or additionally, the
pace of the cuing signals are based on intervals where the
subject's gait is determined to be regular, at least to some
extent.
[0226] In some embodiments, one or more other signals are generated
in addition or instead the audible messages described above. For
example, rhythmic auditory stimulation by tone such as or similar
to a metronome, or rhythmic visual stimulation by one or more of
alarm lights 50 or indications on display 46.
[0227] In some embodiments, detector 20 is augmented to comprise a
vibrator (e.g. akin to some pagers or cellular phones) and
vibrations are generated to indicate the gait situation or provide
pace guiding signals.
[0228] The amount of gait acceleration information that may be made
available for analysis may be greatly increased due to the
convenience provided by near fall detector 20 in this application.
This in turn may lead to improvements in patient cognitive and
motor functioning, particularly since available data suggest that
interventions are more effective when they take place over longer
time periods, are individually tailored, and include exercise in
the home environment.
[0229] Near fall detector 20, in some embodiments of the invention,
may even be incorporated into treadmills or other exercise
equipment, or provided as an add-on accessory. The device could be
in the form of a "smart-box" that contains the software, processor
34, communication hardware, and other elements. When using this
type of exercise equipment, the user could indicate that he or she
is doing a special activity for monitoring for near falls. In some
embodiments the device may adjust the parameter threshold values to
account for planned variations in exercise stimulation, such as
increases in treadmill speed designed to challenge the patient.
[0230] The information provided by the gait acceleration profile
may also provide insight into a person's neurological state related
to the diagnosis of other types of medical conditions besides the
predilection to fall.
[0231] It is hypothesized that a gait profile based on acceleration
and other measures of movement (e.g., gyroscopes, tilt sensors)
that includes such information as near falls, step and stride
regularity, and symmetry may be tracked as part of a patient's
medical record, and used as a tool for therapeutic use.
[0232] For example, in many cases prior to falling, there is an
instant or moment in time when the person's brain fails to operate
properly. In most cases this aspect of the person's medical
condition may not be detectable until the symptoms become more
pronounced and the underlying disease becomes more severe. However,
in some cases the reduced brain activity may be observable
indirectly, through the person's motor output or gait. By
monitoring gait with near fall detector 20 of the present
invention, the person's quality of movement may provide an early
warning indicator of the onset of Parkinson's disease, for example,
or other movement disorders.
[0233] In another example, a physician may have an array of
possible treatments available for a patient diagnosed with a
particular illness. One of the possible treatments may be a drug
that is known to be effective with some patients but not with
others, but for which there is no methodology to discern beforehand
whether a particular patient will benefit. Upon further research
using the gait profile, it may be found that the gait profile
provides the missing neurological information to assist the
physician in determining whether the drug will be effective in that
case. Used in this way, the gait profile may lead to better and
more cost effective medical care. Further, the efficacy of
treatment may be verified by continuing to monitor the gait
profile, and by analyzing subsequent near fall data to confirm that
the number of instances of near falls and/or their magnitude has
declined.
[0234] As used herein the term "about" refers to.+-.10%.
[0235] The terms "comprises", "comprising", "includes",
"including", "having" and their conjugates mean "including but not
limited to".
[0236] As used herein, the singular form "a", "an" and "the"
include plural references unless the context clearly dictates
otherwise.
[0237] Throughout this application, various embodiments of this
invention may be presented in a range format. It should be
understood that the description in range format is merely for
convenience and brevity and should not be construed as an
inflexible limitation on the scope of the invention. Accordingly,
the description of a range should be considered to have
specifically disclosed all the possible subranges as well as
individual numerical values within that range. For example,
description of a range such as from 1 to 6 should be considered to
have specifically disclosed subranges such as from 1 to 3, from 1
to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as
well as individual numbers within that range, for example, 1, 2, 3,
4, 5, and 6. This applies regardless of the breadth of the
range.
[0238] Whenever a numerical range is indicated herein, it is meant
to include any cited numeral (fractional or integral) within the
indicated range. The phrases "ranging/ranges between" a first
indicate number and a second indicate number and "ranging/ranges
from" a first indicate number "to" a second indicate number are
used herein interchangeably and are meant to include the first and
second indicated numbers and all the fractional and integral
numerals therebetween.
[0239] It is appreciated that certain features of the invention,
which are, for clarity, described in the context of separate
embodiments, may also be provided in combination in a single
embodiment. Conversely, various features of the invention, which
are, for brevity, described in the context of a single embodiment,
may also be provided separately or in any suitable subcombination
or as suitable in any other described embodiment of the invention.
Certain features described in the context of various embodiments
are not to be considered essential features of those embodiments,
unless the embodiment is inoperative without those elements.
[0240] Although the invention has been described in conjunction
with specific embodiments thereof, it is evident that many
alternatives, modifications and variations will be apparent to
those skilled in the art. Accordingly, it is intended to embrace
all such alternatives, modifications and variations that fall
within the spirit and broad scope of the appended claims.
[0241] All publications, patents and patent applications mentioned
in this specification are herein incorporated in their entirety by
reference into the specification, to the same extent as if each
individual publication, patent or patent application was
specifically and individually indicated to be incorporated herein
by reference. In addition, citation or identification of any
reference in this application shall not be construed as an
admission that such reference is available as prior art to the
present invention. To the extent that section headings are used,
they should not be construed as necessarily limiting. In addition,
any priority document(s) of this application is/are hereby
incorporated herein by reference in its/their entirety.
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