U.S. patent application number 13/920032 was filed with the patent office on 2014-03-06 for method, apparatus, and system for characterizing gait.
This patent application is currently assigned to APDM, INC. The applicant listed for this patent is Timothy Brandon, Gavin Gallino, Andrew Greenberg, Lars Holmstrom, James McNames, Sean Pearson, Pedro Mateo Riobo Aboy. Invention is credited to Timothy Brandon, Gavin Gallino, Andrew Greenberg, Lars Holmstrom, James McNames, Sean Pearson, Pedro Mateo Riobo Aboy.
Application Number | 20140066816 13/920032 |
Document ID | / |
Family ID | 44505960 |
Filed Date | 2014-03-06 |
United States Patent
Application |
20140066816 |
Kind Code |
A1 |
McNames; James ; et
al. |
March 6, 2014 |
METHOD, APPARATUS, AND SYSTEM FOR CHARACTERIZING GAIT
Abstract
Disclosed embodiments relate to methods, apparatuses, and
systems for characterizing gait. Specifically, disclosed
embodiments are related methods, apparatuses, and systems for
characterizing gait with wearable and wirelessly synchronized
inertial measurement units. These include a method for gait
characterization that comprises (a) detecting zero-velocity periods
using two or more wearable and wirelessly synchronized movement
monitoring devices including a triaxial accelerometer and a
triaxial gyroscope and (b) calculating temporal measures of gait
during walking by estimating the change in position and orientation
during each step.
Inventors: |
McNames; James; (Portland,
OR) ; Pearson; Sean; (Portland, OR) ;
Holmstrom; Lars; (Portland, US) ; Riobo Aboy; Pedro
Mateo; (Portland, OR) ; Greenberg; Andrew;
(Portland, OR) ; Gallino; Gavin; (Portland,
OR) ; Brandon; Timothy; (Beaverton, OR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
McNames; James
Pearson; Sean
Holmstrom; Lars
Riobo Aboy; Pedro Mateo
Greenberg; Andrew
Gallino; Gavin
Brandon; Timothy |
Portland
Portland
Portland
Portland
Portland
Portland
Beaverton |
OR
OR
OR
OR
OR
OR |
US
US
US
US
US
US
US |
|
|
Assignee: |
APDM, INC
Portland
OR
|
Family ID: |
44505960 |
Appl. No.: |
13/920032 |
Filed: |
June 17, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13037310 |
Feb 28, 2011 |
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13920032 |
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12632778 |
Dec 7, 2009 |
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13037310 |
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61660700 |
Jun 16, 2012 |
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Current U.S.
Class: |
600/595 |
Current CPC
Class: |
A61B 5/6831 20130101;
A61B 2560/0475 20130101; H04W 56/002 20130101; A61B 5/0004
20130101; A61B 5/7246 20130101; A61B 5/1101 20130101; A61B 5/002
20130101; A61B 5/112 20130101; A61B 5/0024 20130101; A61B 2562/0219
20130101; H04L 1/08 20130101; A61B 5/4082 20130101; A61B 5/7257
20130101 |
Class at
Publication: |
600/595 |
International
Class: |
A61B 5/11 20060101
A61B005/11; A61B 5/00 20060101 A61B005/00 |
Claims
1. A method for gait characterization comprising: (a) detecting
zero-velocity periods using two or more wearable and wirelessly
synchronized movement monitoring devices, said movement monitoring
devices comprising a triaxial accelerometer and a triaxial
gyroscope with a bandwidth of at least 15 Hz; and (b) calculating
temporal measures of gait during walking by estimating the change
in position and orientation during each step.
2. The method of claim 1, wherein said calculating temporal
measures of gait includes performing template matching based on the
magnitude of said accelerometer's and said gyroscope's signals from
both feet.
3. The method of claim 2, wherein said template matching is
characterized by 1) enabling multiple iterations to refine a final
template, 2) weighting each template by the standard deviation of
said template across detected steps, 3) scaling the template's
error to be equal to one when said movement monitoring devices are
stationary, 5) using a fast method based on a fast Fourier
transform configured for calculating the template's matching error,
or combinations thereof.
4. The method of claim 3, wherein said calculating temporal
measures of gait further comprises: a) detecting initial steps, b)
estimating a gait cycle duration, c) building an initial template,
d) calculating a template match, e) detecting initial template
steps, f) validating steps, g) adding missed steps, or combinations
thereof.
5. The method of claim 4, wherein calculating temporal measures of
gait further comprises measuring asymmetry using time-proximate
steps by combining left and right steps into consecutive left-right
pairs.
6. The method of claim 5, wherein calculating temporal measures of
gait further comprises characterizing gait during normal periods of
walking by isolating sequences of steps in which the subject is
traveling forward on a flat surface based on changes in height,
bank angle, elevation angle, and heading angle.
7. The method of claim 6, wherein calculating temporal measures of
gait further comprises generating an indicator of foot drop and
fall risk by characterizing the pitch of the foot with wearable
sensors at the moments of heel strike and toe-off.
8. The method of claim 7, wherein calculating temporal measures of
gait further comprises characterizing the lateral deviation in a
sequence of two steps resulting in three foot placements based on
how far a middle foot placement deviates from a straight path from
a first to a last foot placement.
9. The method of claim 8, wherein calculating temporal measures of
gait further comprises measuring the lateral swing of the foot
during a single step.
10. The method of claim 9, wherein said method comprises: 1)
upsampling, 2) estimating biases, 3) calculating magnitudes, 4)
finding still periods, 5) calculating positions, 6) detecting
steps, 7) finding and validating step sequences, and 8) calculating
gait metrics.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Continuation-in-Part of U.S. patent
application Ser. No. 13/037,310 filed on 2011 Feb. 28 which is a
Continuation-In-Part of U.S. patent application Ser. No. 12/632,778
filed on 2009 Dec. 7, which claims the benefit of U.S. Provisional
Application No. 61/120,485 filed on 2008 Dec. 7, and are hereby
incorporated by reference in their entirety. This application also
claims the benefit of U.S. Provisional Application No. 61/1,660,700
filed on 2012 Jun. 16, which is incorporated herein by reference in
its entirety.
TECHNICAL FIELD
[0002] Disclosed embodiments relate to methods, apparatuses, and
systems for characterizing gait. Specifically, disclosed
embodiments are related methods, apparatuses, and systems for
characterizing gait with wearable inertial measurement units.
BACKGROUND
[0003] Gait analysis is important in diagnosing and assessing
several neurological diseases such as Parkinson's disease (PD) and
other conditions. Objective, accurate, and fully automated gait
characterization requires novel biomedical signal processing
methods and specialized hardware for continuous movement
monitoring.
A. Objective Assessment of Movement Disorders
[0004] In recent years, large advances have been made in
micro-electro-mechanical systems (MEMS) and inertial sensors. It is
now possible to record body movements for hours with small,
low-power, wearable sensors that include accelerometers,
gyroscopes, goniometers, and magnetometers. Despite these advances,
clinical practice and clinical trials related to movement disorders
are still based on subjective assessment using rating scales. This
is due to the fact that there are no commercially available systems
to perform objective assessment of movement disorders. One of the
main challenges in designing a complete, portable, and easy-to-use
system for objective assessment of movement disorders that would be
appropriate for clinical practice and clinical trials is the
unavailability of movement monitors that can wireless communicate
with each other in order to collect synchronized kinematic data
from different locations such as the ankles, wrists, waist, and
trunk. Currently, there are no movement monitors capable of
performing wireless synchronization of the data collected by the
different sensors and ensuring that the collected data is never
lost during wireless data transmission (i.e. robust wireless data
transfer).
A.1. Subjective Assessment of Movement Disorders and Clinical
Trials
[0005] Subjective assessment of movement disorders using clinical
rating scales or poor instruments of mobility result in clinical
trials that are inefficient, slow, complicated, and expensive. The
primary outcomes are typically self-reported outcomes recorded from
patient diaries (falls), clinician rating scales (UPDRS, Berg
Balance scale), and/or patient questionnaires (PDQ-39). All of
these instruments have limited resolution, are subjective, and are
susceptible to bias. To overcome the limitations of these
instruments, clinical trials typically require a large number of
subjects to detect a clinically significant difference between
groups. The data is typically collected on paper versions of the
scales and questionnaires. The data is then entered into a database
by research assistants, which may result in transcription errors.
Finally, the data from each site is then transmitted to a central
site, so that a statistician can analyze the data and generate the
results of the trial.
A.2. Subjective Assessment of Movement Disorders
[0006] Subjective clinical rating scales such as the Unified
Parkinson's Disease Rating Scale (UPDRS) are the most widely
accepted standard for motor assessment. Presently motor symptoms
are diagnosed and assessed during a brief clinical evaluation
performed by a primary care physician or neurologist every 3-6
months. Current methods of motor system assessment for PD are
inadequate because they are intermittent, coarse, subjective,
momentary, stressful to the patient, and insensitive to subtle
changes in the patient's motor state. These scales can only be
applied in clinical settings by trained clinicians.
[0007] Patient diaries and other methods of self reporting are
sometimes used to determine patients' motor condition throughout
the day, but these are often inaccurate, incomplete, cumbersome,
and difficult to interpret. These methods are also susceptible to
selection, perceptual, and recall bias. Patients generally have
poor consistency and validity at assessing the clinical severity of
their impairment. Patients with mild or moderate dyskinesia may be
unaware of their impairment and may have poor recall. However,
patients may be able to accurately monitor their overall
disability.
A.3. Objective Assessment of Balance, Gait, and Fall Risk
[0008] Neurological deficits, such as Parkinson's disease,
inevitably result in limitations on mobility, a sensitive measure
of health and a critical element for independent living and quality
of life. However, clinical practice aimed at reducing mobility
disability have been limited either by insensitive, descriptive
balance rating scales, timed tests of gait speed, fall counts or by
complex, expensive, and time-consuming laboratory assessments of
balance and gait. For instance, the lack of accurate objective
measures of balance and gait greatly impedes the development and
testing of new treatments to improve mobility in neurological
patients.
[0009] As an example, movement disorders such as balance and gait
disorders, are the most common cause of falls and reduced quality
of life in people with neurological disorders. People with
Parkinson's disease (PD) fall more often than any other
neurological disease with 43-70% falling each year. Fear of falling
leads to activity restriction and declines in mobility. However, no
system currently exists that allows clinicians to evaluate fall
risk based on objective tests of balance and gait in a clinical
environment.
[0010] Up to 52% of healthy older adults experience a fall each
year. Falls are costly, both financially and in terms of quality of
life. Financially, one in four falls necessitates use of health
care resources. In addition, fear of falling often leads to
self-induced activity restriction and declines in mobility status
and emotional well being. Although the cost of falls in patients
with all neurological disorders has not been explicitly delineated,
people with Parkinson's disease have a 57% higher prevalence of
falls and injuries than same age control subjects. This is
especially significant given the cost of falls, which in 1996
apparently exceeded $9 billion spread across 225,000 older
Americans.
B. Movement Monitors
[0011] State of the art movement disorder monitors employ inertial
sensors, such as accelerometers and gyroscopes, to measure
position, velocity and acceleration of the subject's limbs and
trunk. Current monitors fall into two classes, namely activity
monitors and inertial monitors, both of which have disadvantages
and limitations that make them incapable of continuous monitoring
of movement disorders or objective monitoring.
[0012] Activity monitors, such as in U.S. Pat. No. 4,353,375,
collect low frequency and low resolution samples of the subject's
gross activity for days to weeks at a time. These monitors are
usually small, unobtrusive devices resembling watches or brooches
which are worn by the subject for long periods of time such as days
or weeks outside of the clinical setting. They measure movement
using low quality inertial sensors at low sampling frequencies, and
usually measure only a few degrees of freedom of motion instead of
all six possible degrees of freedom of motion. The low quality
measurements are stored in data storage on-board the device which
is later downloaded and analyzed. While they are useful for
recording the gross activity levels of the subject, and they may be
comfortable and unobtrusive enough to be worn by the subject for
longs periods of time, they are only useful in measuring non-subtle
symptoms of movement disorders such as activity versus rest cycles.
Subtle symptoms, such as symptom onset and decline, or non-obvious
symptoms such as bradykinesia, can not be measured by these
devices. These devices, also known as actigraphers, typically
measure movement counts per minute which make even simple
determinations such as determining the wake-up time challenging.
Consequently, actigraphers are inappropriate for continuous
ambulatory monitoring of movement disorders such as in Parkinson's
disease.
[0013] Inertial monitors, such as in U.S. Pat. No. 5,293,879,
collect high frequency, high resolution samples of the subject's
movements for short periods of time. These devices are larger and
more obtrusive, resembling small boxes which are worn by the
subject for short periods of time such as hours, or at most, a day,
and usually in clinical settings. They measure movement using high
quality inertial sensors, and usually include all six degrees of
freedom of motion (three linear axes and three rotational axes).
Inertial monitors may store the inertial measurements in the device
for later analysis, or they may use telemetry radios to wirelessly
transmit the measurements in real-time to a nearby computer or
recording device. These devices are useful for measuring all
symptoms of movement disorders, but because of their larger,
obtrusive size and short operational times, they are not useful for
measuring symptoms outside of clinical settings or for long periods
of time.
[0014] Movement disorder monitoring can be enhanced by monitoring
multiple locations on a subject at the same time. Current systems
either do not synchronize their measurements, or require wires to
synchronize sampling. Additionally, current movement disorder
monitoring devices also lack aiding sensors, such as absolute
measures of position.
[0015] Movement monitoring devices and systems that overcome
challenges of physical size, power consumption, and wireless
synchronization are currently unavailable and have significant
potential in numerous applications including clinical practice and
research.
[0016] Currently, the most common and accurate method of tracking
movement is based on optical motion analysis systems. However,
these systems are expensive, can only measure movements in a
restricted laboratory space, and cannot be used to observe patients
at home.
[0017] Current inertial monitoring systems can be divided into
three categories: computer-tethered, unit-tethered, and untethered.
Computer-tethered devices connect the sensor directly to a
computer. One of the best systems in this category is MotionNode
(GLI Interactive LLC, Seattle). These systems are not practical for
home settings. Unit-tethered systems connect the sensors to a
central recording unit that is typically worn around the waist.
This unit typically houses the memory, batteries, and wireless
communications circuits. Currently, these systems are the most
widely available and are the most common in previous studies. One
of the best systems in this category is the Xbus kit (Xsens,
Netherlands). This system includes up to five sensors, each with
high-performance, triaxial accelerometers, gyroscopes, and
magnetometers. The system can operate continuously and wirelessly
stream data via Bluetooth to a laptop for over 3 h at distances up
to 100 m. However the system is too cumbersome and difficult to use
in a home study due to the wires connecting the sensors and central
recording unit, the battery life is too short, and the
interconnecting wires may be hazardous during normal daily
activities. The typical untethered system combines the batteries,
memory, and sensors in single stand-alone units. The only wireless
untethered systems reported in the literature are "activity
monitors," which measure the coarse degree of activity at intervals
of 1-60 s, typically with a wrist-worn device that contains a
single-axis accelerometer. These devices are sometimes called
actigraphs or actometers. Most of these devices only report
activity counts, which are a measure of how frequently the
acceleration exceeds a threshold. Some custom activity monitors
directly compute specific metrics of motor impairment, such as
tremor. A few studies have shown that activity monitors worn over
5-10 days could detect on/off fluctuations, decreased activity from
hypokinesia, and increased activity associated with dyskinesia.
However, typical activity monitors cannot distinguish between motor
activity caused by voluntary movement, tremor, or dyskinesia. They
do not have sufficient bandwidth, memory, or sensors for precise
monitoring of motor impairment in PD. They also cannot distinguish
between periods of hypokinesia and naps.
[0018] Recently, Cleveland Medical Devices (Cleveland, Ohio)
introduced two untethered systems, the KinetiSense and Kinesia
devices. These systems include triaxial accelerometers and
gyroscopes with bandwidths of 0-15 Hz, but lack magnetometers.
Although large, the central recording units could to be worn on the
wrist. The sensor and recording unit can be connected to form a
single unit. This devices can record data continuously and store it
on an on-board memory for up to 12 h. However, 1) the due to their
size it is difficult for several of these devices to be used at the
same time (e.g. wrist, ankle, waits, trunk), 2) the storage
capability is limited to a single day and consequently it is
difficult to conduct multiple day studies, and 3) the devices are
not synchronized.
[0019] Movement monitoring devices and systems that overcome the
challenges of 1) physical size (volume), 2) power consumption, 3)
wireless synchronization, 4) wireless connectivity, 5) automatic
calibration, and 6) noise floor; are currently unavailable and have
significant potential in numerous applications including clinical
practice and research. Finally, the limited solutions currently
available are device-centric and do not include a complete platform
to perform collection, monitoring, uploading, analysis, and
reporting.
C. Movement Monitors with Wireless Synchronization
[0020] While there are several commercial movement monitors
available capable of wireless data transmission, currently none of
these movement monitors is capable of providing wireless
synchronization of the sampling instances. The most advanced
inertial monitors capable of wireless data transfer such as Xsens'
full body motion capture monitor (XSens Technologies) require wires
between each of the movement monitors and a central unit in order
to synchronize the sampling instances of each of the monitors.
Synchronization is critical for applications where more than one
movement monitor is needed.
[0021] Wireless sensor networks have multiple independent nodes all
sensing environmental factors at the same time. In the case of a
wearable wireless movement monitor, these environmental factors are
the kinetic state of the various limbs of a subject wearing two or
more movement monitors. Later, during data analysis, the samples of
the two or more movement monitors must correlated in time to make
any sense together. For example, two movement monitors on the
ankles need to be correlated in time in order to show the
difference between a lopsided gallop and a smooth run. The problem
is that in order to be correlated in time, the sensors must sample
at the same time, and, over time, at the same rate, over a long
time period of hours, or even days.
[0022] There are many ways to do this correlation, but the
challenge with small wireless sensor systems is how to go about
providing this synchronization of the sampling time and rate
without unduly impacting other system parameters.
[0023] One way in which current wireless sensor networks
synchronize with each other is to provide a wired sync line between
nodes. While simple and effective, this not only requires
cumbersome wires running between nodes, but obviously defeats the
wireless part of the wireless sensor network.
D. Movement Monitors with Robust Wireless Data Transfer
[0024] In small, highly mobile wireless devices, such as wireless
movement monitors, it is necessary to robustly stream large amounts
of data (100s of bits to 100s of kilobits per second) in near real
time (without large latencies in transmission) over a radio
frequency communication channel. These continuous, real-time
wireless transmissions often suffer from unpredictable data loss
due to a variety of environmental factors, including distance
between transmitter and receiver, absorption of the signals by
local materials (including human bodies), multipath interference
due to objects which reflect or refract signals, and even
interference from other devices. The challenge with these small
embedded systems is how to go about guaranteeing transmission of
the signal without unduly impacting other system parameters.
[0025] One way in which current wireless movement monitors overcome
transmission problems, such as distance and interference, is to
increase the radio frequency (RF) signal strength of their
transmissions and/or to use receive amplifiers. Either method leads
to an large increase in consumed power, which leads to larger
battery sizes, which leads to dramatically larger and heavier
devices, forcing some systems to even have large, separate wired
unit which holds a replaceable battery pack.
[0026] None of the current methods to overcome radio communication
disruptions allows a wireless sensor to remain small, reduce power
consumption, and avoid data loss during long interruptions in
communication.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] Disclosed embodiments of example results are illustrated by
way of example, and not by way of limitation, in the figures of the
accompanying drawings.
[0028] FIG. 1 shows a block diagram of the gait characterization
method according to one embodiment.
[0029] FIG. 2 shows a block diagram of the gait characterization
method according to an alternative embodiment.
[0030] FIG. 3 shows a block diagram of the template matching method
according to one embodiment.
[0031] FIG. 4 illustrates the initial and final location of the
foot during a single step with the left side. The straight vertical
line shows the forward direction of travel. The two angles
.theta..sub.i and .theta..sub.e show the toe out angles at the
beginning and end of the step. The average of these two angles is
reported by the method as the toe out angle for this step.
[0032] FIG. 5 shows a sequence of three foot placements. A straight
line path from the first to the last foot placement is considered
the forward direction of travel. The lateral step deviation is
calculated as the maximum lateral distance of the middle step from
this path.
[0033] FIG. 6 illustrates a table of results produced by the gait
characterization method including the output metrics. This includes
the number of step sequences used to calculate each gait metric
(column n), the average (.mu.) and standard deviation (.sigma.) of
each metric for the left foot, the right foot, and the left-right
differences. Each of these six statistics is listed for all sixteen
metrics of gait included in this table.
[0034] FIG. 7 shows a heatmap of averaged step trajectories versus
the time of each detected step. The top row shows the forward
position, the middle row shows the amount of lateral swing, and the
bottom plot shows the vertical position of the top of the foot
during swing.
[0035] FIG. 8 shows the average morphology of the accelerometer and
gyroscope magnitudes during a step. The shaded region shows the
variability. This shows the shape of the four-channel templates
used for template matching.
[0036] FIG. 9 shows the weighted and scaled template error versus
time for an example recording. The lower horizontal lines show the
initial thresholds for detecting minima in the error that represent
steps. The higher horizontal lines show the thresholds used to add
missed steps during the template matching method. The bottom row of
plots shows the intervals between steps. The occasional spikes that
are roughly 20 s apart are due to the slowing in the gait cycle
that occurs when the subject made 180 degree turns.
[0037] FIG. 10 shows the initial detection of steps by vertical
black lines based on still periods of the other foot as detected by
the gyroscopes and accelerometers. The horizontal lines show
thresholds. The bottom set of plots shows the intervals between the
initial detection of steps. Once the initial detection of steps is
completed, an initial template can be created to begin the
iterative template matching detection.
[0038] FIG. 11 shows the actual templates for a real subject. The
left column of plots shows the stance side and the right column of
plots shows the swing side. The top row of plots shows the
accelerometer magnitudes and the bottom shows the gyroscope
magnitudes. The width of the shaded regions shows the standard
deviation across the detected steps that were used to create the
template. The thick dark lines show the actual templates used for
step detection.
[0039] FIG. 12 shows another example of the template error for the
left and right feet. At the beginning and end of the recording the
subject was still and the normalized error was equal to 1, as the
method is designed. The horizontal lines show the thresholds. The
green dots show the individual detected steps.
[0040] FIG. 13 shows the error versus the shift in alignment of the
right template relative to the left template. The minimum is shown
by the red dot and represents the best shift to align the left and
right templates.
[0041] FIG. 14-29 shows illustrative examples of the apparatus and
overall system for wireless synchronized movement monitoring.
DETAILED DESCRIPTION
1) Wireless Synchronization of Sampling Time Instances in Movement
Monitors
[0042] The teachings of this disclosure directed to the calculation
of temporal measures of gait such as single support time require a
particular type of movement monitor (in this disclosure the
concepts of movement monitor, movement sensor, and inertial
measurement unit are considered synonyms and are used
interchangeably). Specifically, it requires wearable movement
monitors characterized by being 1) wearable, 2) untethered, 3)
capable of wirelessly synchronizing the sampling time instances of
two or more monitors (preferable with a synchronization
resolution.gtoreq.1 ms), and 4) having a bandwidth higher than 15
Hz. The details relating to such movement monitors are found in
U.S. patent application Ser. No. 13/037,310 filed on 2011 Feb. 28
entitled "Wireless Synchronized Movement Monitor and System" which
is hereby incorporated by reference.
2) General Description of Method and Apparatus for Characterizing
Gait
[0043] According to one embodiment the method for gait
characterization comprises: (a) detecting zero-velocity periods
using two or more wearable and wirelessly synchronized movement
monitoring devices, the movement monitoring devices comprising a
triaxial accelerometer and a triaxial gyroscope with a bandwidth of
at least 15 Hz; and (b) calculating temporal measures of gait
during walking by estimating the change in position and orientation
during each step. In a more particular embodiment, the step of
calculating temporal measures of gait includes performing template
matching based on the magnitude of the accelerometer's and
gyroscope's signals from both feet. Furthermore, template matching
is characterized by 1) enabling multiple iterations to refine a
final template, 2) weighting each template by the standard
deviation of the template across detected steps, 3) scaling the
template's error to be equal to one when the movement monitoring
devices are stationary, 5) using a fast method based on a fast
Fourier transform configured for calculating the template's
matching error, or combinations thereof. In a particular
embodiment, and without limitation, the step of calculating
temporal measures of gait further comprises: a) detecting initial
steps, b) estimating a gait cycle duration, c) building an initial
template, d) calculating a template match, e) detecting initial
template steps, f) validating steps, g) adding missed steps, or
combinations thereof. In some embodiments, the step of calculating
temporal measures of gait further comprises 1) measuring asymmetry
using time-proximate steps by combining left and right steps into
consecutive left-right pairs, 2) characterizing gait during normal
periods of walking by isolating sequences of steps in which the
subject is traveling forward on a flat surface based on changes in
height, bank angle, elevation angle, and heading angle, 3)
generating an indicator of foot drop and fall risk by
characterizing the pitch of the foot with wearable sensors at the
moments of heel strike and toe-off, 4) characterizing the lateral
deviation in a sequence of two steps resulting in three foot
placements based on how far a middle foot placement deviates from a
straight path from a first to a last foot placement, and 5)
measuring the lateral swing of the foot during a single step. In a
particular embodiment, the overall method for gait characterization
comprises 1) upsampling, 2) estimating biases, 3) calculating
magnitudes, 4) finding still periods, 5) calculating positions, 6)
detecting steps, 7) finding and validating step sequences, and 8)
calculating gait metrics (FIG. 1 and FIG. 2). According to specific
embodiments, the disclosed method can be implemented in other
hardware besides a digital computer including microcontrollers,
processors, DSPs, FPGAs or ASICs, and firmware.
3) Description of Method and Apparatus for Characterizing Gait
According to Particular Embodiments
[0044] In the following description the term "Subject oriented"
describes a reference frame defined as the forward direction in
which the subject is traveling (x-axis) projected onto the plane
that is orthogonal to gravity, the subject's left side that is
orthogonal to gravity and the forward direction (y-axis), and the
up direction defined as the opposite direction of gravitational
attraction (z-axis). This is sometimes briefly described as
forward-left-up. This can be calculated by rotating the position in
the Earth frame (north-west-up) about the z-axis (i.e., changing
the heading angle). The origin of the subject-oriented frame is the
still period when the foot is level on the ground preceding a step
or sequence of steps. The forward direction defining this reference
frame can be defined based on the final position of the foot after
a single step or a sequence of steps. The term "rotational
magnitude" describes the norm of the three gyroscope channels,
possibly after processing to account for calibration, temperature
compensation, upsampling, and bias removal. Specifically this is
defined as the square root of the sum of the three squared
gyroscope channels. When the wearable device is stationary or
still, the magnitude is expected to be close to zero. The term
"accelerometer magnitude" describes the norm of the three
accelerometer channels, possibly after processing to account for
calibration, temperature compensation, upsampling, and bias
removal. Specifically, this is defined as the square root of the
sum of the three squared accelerometer channels. When the wearable
device is stationary or still, the magnitude is expected to be
close to the acceleration due to gravity, which is approximately
9.8 m/s.sup.2. The term "step pair" describes a pair of normal
steps consecutive in time without delay or pause of either the left
side followed by the right side, or the right side followed by the
left side. Paired steps are helpful for statistical comparisons of
the gait between the left and right sides. The term inertial
measurement unit (IMU) describes a device containing at least
triaxial accelerometers and triaxial gyroscopes with a bandwidth of
at least 15 Hz.
[0045] The following sections describe various embodiments to
implement a method, apparatus, and system for gait characterization
based on IMUs. According to one particular embodiment, and without
limitation, the method for characterizing one or more features of
gait during walking from two or more wirelessly synchronized IMUs
attached to the feet or shoes based on detected steps comprises
detecting zero-velocity periods and estimating the change in
position and orientation during each step using inertial navigation
with or without aiding. In a particular embodiment, the method
further comprises estimating the bias of the accelerometers by
detecting still periods, estimating the attitude of the sensor,
subtracting the effect of gravity, calculating the residual--which
is expected to be bias (slowly varying) and white noise (broad
band), and calculating the bias in between still periods with some
form of interpolation or smoothing. In a particular embodiment, the
method comprises detecting each step by initially detecting still
periods based on low rotational magnitude and an accelerometer
magnitude that is close to that of gravity, applying prior
knowledge of still periods during the gait cycle to eliminate
implausible gait periods, estimating an initial template from the
initially detected periods based on one or more of the
accelerometer or gyroscope signals from either of the feet,
calculating a figure of merit by comparing the shifted template to
signal segments over the full range of the signal, and detecting
minima or maxima in the figure of merit to determine step
locations. This embodiment comprises estimating the variability of
the template, and weighting the figure of merit based on the
variability of the template. In a particular embodiment, the method
further comprises calculating the figure of merit for two or more
of the four magnitudes (accelerometer magnitude and gyroscope
magnitudes for each foot), scaling the figure of merit for each
magnitude such that during a still period the error is equal to a
constant, and combining the figure of merit for the different
magnitudes with a statistic such as the average, median, minimum,
or maximum. In a particular embodiment, the method further
comprises estimating the template locally in time using either a
fixed window or a fixed number of steps that are nearby in time to
the time at which the template error is calculated, and computing a
subject-oriented reference frame for calculation of the foot
position and orientation during gait comprised of estimating the
orientation and position of the foot in an inertial reference frame
(such as the Earth reference frame of north-west-up or
north-east-down), defining the forward direction of gait based on
the change in position over one or more steps, translating the
inertial reference frame to an origin defined as the starting
location of one or more steps, and rotating the inertial reference
frame to have a forward axis calculated from the change in position
from the starting location to the ending location after one or more
steps in the plane orthogonal to the up direction defined by
gravitational attraction. In particular embodiments, and without
limitation, the method further comprises calculating the following
continuously during each step: the lateral (leftward) position of
the foot, the height of the foot (defined relative to the location
of the wearable device), the forward position, the heading angle
(defined relative to forward in the forward-left plane), the
elevation angle (defined as extent of upward tilt relative to the
forward-left plane), and the bank angle (defined as the remaining
Euler angle). Additionally, in particular embodiments, the method
further comprises detecting step pairs by detecting candidate steps
on each side and pairing steps that meet known normal physiologic
criteria such as the period of time between the start of a step on
one side and the start of a step on the other side. Certain
embodiments, further comprise detecting the time at which the toe
leaves the ground (toe off) based on the time of the maximum
subject-oriented elevation Euler angle, detecting the time at which
the foot is parallel to the ground during the swing phase of a gait
cycle based on the time at which the subject-oriented elevation
Euler angle is near zero, detecting the time at which the heel
strikes the ground based on the minimum subject-oriented elevation
Euler angle and a large acceleration magnitude, calculating the
standard division of the gait cycle from wirelessly synchronized
triaxial IMUs attached to the feet or shoes into relative durations
for the initial double support, single support, terminal double
support, initial & mid swing, and terminal swing from the
detected toe-off, foot flat, and heel strikes for both feet,
calculating the orientation of the foot during still periods which
may be used to calculate the extent of pronation, and calculating
sequences of consecutive steps in the forward direction to
characterize normal gait during periods that exclude starts, stops,
turns, pauses, and other interruptions to normal forward gait.
According to one embodiment, an apparatus comprises a processor
configured to perform the method steps above described and hardware
to display the results. A system comprises the method, the
apparatus, and a plurality of wearable synchronized movement
monitors (FIG. 14-29). The following sections describe in more
detail the particular method steps involved in the various
embodiments of the method, apparatus, and system.
[0046] The following sections provide additional detailed
information for particular embodiments, and without limitation, of
the method disclosed in FIG. 1 and FIG. 2.
3. A. Upsampling
[0047] According to one embodiment, the first stage of the gait
characterization method upsamples the raw sensor data to an
effective sampling rate that is high enough to prevent significant
errors in the integration caused by first order approximations
(i.e., the Euler method) of integrals. One skilled in the art will
know there are other methods that could be used to estimate
nonlinear integrals that may require less computation or have other
advantages. According to one embodiment, and without limitation,
the method uses a bandlimited interpolation methodology to upsample
the signals, though many other largely equivalent methods are
available. In one embodiment, after upsampling, the sample rate
should be 500 Hz or higher, roughly 10.times. the bandwidth of the
signal. Further improvements are possible with resampling to higher
rates.
3. B. Zero-Velocity Detection
[0048] Detection of periods when the IMUs and feet are still is
used in one embodiment of the method in several stages of the
signal processing. These still periods are often referred to as
zero-velocity periods in the literature and the algorithms for
detecting them are called zero-velocity detectors. When the sensors
are placed on the feet, these still periods normally occur during
gait when the foot is flat on the ground.
[0049] According to one embodiment, and without limitation, the
method detects these still periods by calculating the Euclidean
magnitude, or norms, of the gyroscopes and magnetometers. These
magnitudes are expected to be zero for the gyroscopes and equality
to the magnitude of gravity for the accelerometers. The magnitudes
are convenient to work with because they are independent of the
sensor orientation. Three thresholds are specified for the minimum
and maximum magnitudes of the accelerometers and the maximum
magnitudes of the gyroscopes. If any of the threshold criteria are
not met, the IMU is declared as moving. If all of the threshold
criteria are met, the IMU is declared as still.
[0050] Some stages of processing require detection of periods that
are more stationary than others. For example, estimation of the
sensor bias requires periods that are very still. Estimation of the
IMU attitude by determining the direction of gravity relative to
the IMU's body orientation, requires still periods that can be less
still.
[0051] It is possible to improve performance by smoothing either
the signals before magnitude calculation or smoothing the magnitude
signals. Smoothing may be implemented with a lowpass filter, kernel
smoother, or any of a variety of other methods. The extent of
smoothing may vary depending on the requirements of the processing
stage. Alternative embodiments make use of these techniques to
improve performance.
[0052] Using a simple threshold detection can result in detecting
still periods in which there is slight movement near the crossing
points of the thresholds. Performance may be improved by finding
the first minimum in the magnitude signal, with or without
smoothing, after crossing the threshold to eliminate these slight
periods of movement.
[0053] In some cases the still period is expected to be of a
certain duration. Performance may be improved by specifying an
additional threshold on the still duration required in order for a
still period to be considered valid and usable for a given stage of
signal processing.
3.C. Bias Estimation
[0054] According to one embodiment, the next stage of the method
estimates the sensor bias. In one particular embodiment, and
without limitation, this processing stage begins by finding very
still periods in which tight thresholds are used to detect the
still periods. During still periods the gyroscopes are expected to
contain a slowly varying bias and broadband, zero-mean noise. The
bias can be estimated during still periods with a lowpass filter or
equivalent means of estimating the slowly varying component. If the
periods are brief enough, as a constant estimated as the mean,
median, or some other measure of central tendency.
[0055] During still periods the accelerometers are expected to
contain a constant component due to gravity, a slowly varying bias,
and broadband noise. The component due to gravity is expected to be
much larger in magnitude and can be used with techniques to
estimate the attitude (elevation and bank angles, but not heading)
of the IMU. The attitude is combined with knowledge of the
magnitude of gravity (approximately 9.8 m/s.sup.2 at most
locations) to estimate the expected gravitational component of the
accelerometers, which can then be subtracted from the accelerometer
signals during each of the still periods. The difference is
approximately comprised of just the slowly varying bias and
broadband, zero-mean noise. As with the gyroscopes, the
accelerometer bias can be estimated during still periods with a
lowpass filter or equivalent means of estimating the slowly varying
component. If the periods are brief enough, as a constant estimated
as the mean, median, or some other measure of central tendency.
[0056] Once the gyroscope and accelerometer biases are estimated in
each of the still periods, any form of smoothing or interpolation,
such as piecewise linear interpolation, kernel smoothing, a spline,
or quadratic interpolation, can be used to estimate the bias when
the IMU is not still. Once the sensor biases are estimated, they
can be subtracted from the entire observed signals to produce
signal estimates that are largely immune to the effects of
bias.
3.D. Step Position Tracking
[0057] According to one embodiment, once still periods are
detected, inertial navigation methods can be used to estimate the
orientation and position of the IMU and foot during each transition
between one still period and the next. Note that a transition
between still periods may or may not correspond to a normal step.
The method for tracking the position of a step requires an initial
estimation of the IMU orientation. The methods uses the first still
period and the accelerometers to determine the attitude. The
heading may be defined arbitrarily. If a magnetometer is available,
it may be used to determine the heading and possibly improve the
initial attitude estimate. The direction of gravity is used to
define the upward direction in the reference frame used for
position tracking, often called the inertial reference frame or the
Earth reference frame.
[0058] In navigation applications it is common to use a reference
frame defined as north (x), east (y), down (z), which satisfies the
right hand rule. In this application it is more convenient and
natural to use an inertial reference frame defined as north (x),
west (y), up (z), which also satisfies the right hand rule.
[0059] According to one embodiment, the method uses quaternions to
represent and track changes in the orientation from one still
period to the next. Forward and backward estimates are calculated
separately and then combined statistically based on the estimated
variance of the two estimates. When state space tracking methods
are used, this method of estimation is called smoothing. Once the
smoothed orientation estimates are calculated, the position
estimates are also computed forward and backward in time by
rotating the accelerometer signals into the inertial reference
frame, subtracting the gravitational component, and then
integrating twice in time to convert acceleration estimates into
position estimates. In a particular embodiment, and without
limitation, during each still period the IMU attitude is updated by
using the gravitational component of the accelerometers to
determine where the upward direction is. The heading is unchanged,
though it could also be updated if magnetometers or some other
absolute reference indicating the IMU heading is available. During
the backward phase of orientation estimation, the updated attitude
is used as the starting point for the orientation estimate.
3.E. Initial Step Detection
[0060] In a particular embodiment, once the bias is estimated and
subtracted from the signals, the method detects candidates for
steps. During normal forward walking a period of single support is
expected in which one foot is swinging forward while the other foot
is stationary on the ground. The method detects these periods
initially by finding still periods in which one foot is on the
ground using the method described previously. The still periods are
then checked against a variety of criteria to ensure they
correspond to a step. For example if two minima are adjacent in
time with a duration less than that expected to be physiologically
possible for the duration between two steps, the minimum with a
larger magnitude of movement, as measured for example by the
gyroscope magnitude, is removed. It is also expected that between
still periods for one foot, the other foot will go through a swing
phase that will include a certain amount of movement. Thus the
maximum accelerometer and gyroscope magnitudes during the swing
phase are compared to thresholds to ensure that the foot taking the
step undergoes sufficient movement to qualify as a step. This helps
eliminate candidate periods in which both feet are still.
3.F. Template Matching
[0061] FIG. 3 shows the method steps of the template detection
method according to one embodiment. In a particular embodiment, and
without limitation, once the initial steps are detected, a three
step template matching method is used to more precisely detect all
of the steps. During the first step, a template is estimated. This
particular embodiment of the method uses a four channel template
which includes the accelerometer and gyroscope magnitudes for each
of the feet. The template is computed locally in time based on a
specified number of detected steps that have occurred before and
after the time of interest for the template matching. The duration
of the template is user-specified. In this implementation, the
template spans from half a gait cycle prior to the center of the
template to half a gait cycle after the template. For each channel
and each point in time for the template, the mean across all steps
and the standard deviation across all steps is estimated. One with
ordinary skill in the art will know that other measures of central
tendency, such as the median, and measures of variability, such as
the inter-quartile range, could be used instead.
[0062] In a particular embodiment, during the second step, for each
channel and each point in time, a weighted error, or some other
figure of merit, is computed that corresponds to the degree of
similarity between a signal segment and the template. The error is
weighted by the variability of the template. In this embodiment,
the method uses a weighted mean squared error, but other similarity
measures, weighted or unweighted, could be easily used. The error
for each channel is scaled by the error that occurs for constant
magnitudes in each channel. In this manner, the error for each
channel is normalized so that it has a value of 1 when a constant
signal is applied. This makes it easier to select detection
thresholds that do not vary with the step morphology. Finally, the
template errors from all four channels are combined. In this
embodiment, the method combines them by calculating the average,
but other statistics such as the median, max, or min, could be
used. Finally, a threshold is applied to detect the initial
candidate steps based on template matching. During the third step
logic based on domain knowledge is used to revise and correct the
steps detected by applying a threshold to the detected minima in
the threshold error. For example, if two minima or adjacent in time
by a duration that is shorter than is physiologically possible for
normal gait, the minimum with the smaller template error is
retained and the one with the larger error is eliminated.
[0063] The template error is expected to increase significantly in
between steps. The maximum error between two candidate steps is
compared to a threshold. If the transition error is not as large as
expected, the candidate step is eliminated.
[0064] In a particular embodiment, a forward search and backward
search is also used to find steps with template errors larger than
the initial threshold. A second, higher threshold, is applied to
cases when the separation of two detected steps is larger than
would be expected by a normal gait cycle. A search is performed for
a template minimum over an interval when the expected next step is
expected to occur. If a minimum is found that is lower than the
second, higher template error threshold, and the candidate step
meets other criteria for the expected transition amplitude and gait
cycle duration, then the new step is added. This search is
performed forward in time and backward in time to search for steps
that were missed during the template matching. The three steps
comprising template matching may be repeated for multiple
iterations, with the newly detected steps replacing the initial
steps during each iteration. This can improve both the accuracy of
the times at which the steps are detected as well as the morphology
of the templates.
[0065] In a particular embodiment, and without limitation, the
detection of steps by the left and right feet can be computed
separately. However, to compare the symmetry during subsequent
processing, it's important that the left and right templates be
aligned with one another. Once one side has been processed and the
template finalized, it can serve as a reference to align the
template of the other foot. The alignment can be performed by
computing the template error for a variety of shifted templates and
the shift with the minima error can be selected.
3. H. Detection of Step Pairs
[0066] In order to characterize normal walking, and particularly
gait asymmetry, it is useful to consider pairs of steps for the
left and right sides. In a particular embodiment, the method begins
with the steps detected on the left side and searches for steps on
the right side that most immediately follow. In alternative
embodiments, the method could instead or additionally search for
pairs of right-left steps. Each candidate pair of steps is then
evaluated for a variety of criteria to ensure the step pair is
valid. For example prior to each step the still period is compared
to thresholds for maximum and minimum durations known to occur
during normal walking. Similarly, the transition from one still
period to the next for each foot is evaluated to make sure the
duration is not shorter or longer than is known to occur during
normal walking. The delay from the step on one side to the step on
the next side is also compared to the minimal and maximal values
that are expected to occur during normal walking. Pairs of steps
that pass all of the evaluation criteria are then used for
subsequent processing.
3.I. Detection of Step Sequences
[0067] Characterization of some aspects of gait, such as walking
forward normally, requires processing of sequences of 1 or more
consecutive steps. For example, to determine the direction of
forward motion and to compute the variability in the lateral
(left-right) position of each step, two steps (three still periods)
are required so that the forward direction can be defined as the
path from the first still period to the last still period and the
lateral placement can be determined from the location of the foot
during the intermediate (second) still period.
[0068] In one embodiment of the method, detection of sequences
begins with step pairs as candidates. Pairs that are neighboring in
time are considered as members of the sequence. As with the earlier
stages of processing, candidate sequences are evaluated initially
for a variety of criteria to ensure the sequences comprise normal
forward steps. These criteria include maximal and minimal allowed
durations between steps. Further criteria acceptance criteria are
applied in later stages of processing.
3. J. Transform Inertial Reference Frame to Subject Reference
Frame
[0069] According to one embodiment, once a candidate sequence of
steps is identified, the method defines a new subject oriented
reference frame. The origin is defined as the starting location of
the foot before the first step is taken. As with the earth
reference frame, gravity is used to define the upward direction (z
axis). The direction from the origin to the resting location of the
foot after the final step in the sequence projected onto the plane
that is orthogonal to the z axis is defined as the heading (x
axis). The direction orthogonal to the plane defined by the z and x
axes that satisfies the right hand rule is defined as the left (y
axis), which satisfies the right hand rule. Each step sequence is
rotated from the inertial reference frame to the subject reference
frame through a rotation about the z axis, which is common to both
reference frames.
[0070] In one embodiment, the transition of IMU orientations
between the starting and final location of the foot during a
sequence is computed relative to the starting orientation. This
produces changes in orientation that are relative to the starting
period in which the foot is flat. These orientations are converted
to traditional navigation Euler angles, which can be interpreted in
terms of heading, elevation, and bank angles or in terms of angles
familiar to those who practice gait analysis.
3. K. Detection of Gait Cycle Components
[0071] In one embodiment of the method, the trajectory of
orientations and positions during each step is used to detect
different points in time during gait that are physiologically
meaningful. For example, the time at which the toe leaves the
ground at the beginning of a swing period, the time at which the
foot is level with the ground during the middle of the swing
period, and the time at which the heel strikes the ground at the
end of the swing period can be detected. Specifically the time of
toe off can be approximated as the time at which the elevation
angle (i.e. pitch) is maximal and the time of heel strike can be
approximated as the time at which the elevation angle is minimal.
Alternatively, the heel strike can be detected from the
accelerometer or the rapid deceleration in the Earth reference
frame at the time of heel strike. The toe off can also be detected
from the change in elevation and knowledge of the location of the
IMU relative to the end of the foot. Once the toe off, foot level,
and heel strike phases are identified, the periods of the gait
cycle can be delineated. Specifically the periods of stance, which
include initial double support, single limb support, and terminal
double limb support can be estimated. Also the swing phase of gait
can be estimated as initial+middle swing as the period from toe off
until the foot is horizontal and the terminal swing as the period
from foot horizontal to heel strike. These periods can be expressed
in units of time or as a percentage of the overall gait cycle, the
latter of which is generally preferred. It should be noted that
synchronization of the sensor signals is essential to accurately
calculate these periods.
3.L. Calculation of Metrics
[0072] FIG. 6 shows the statistical summary of gait metrics
according to one embodiment. According to one embodiment, once the
subject oriented foot trajectories of changes in position and
orientation are determined, a variety of metrics can be easily
calculated. This embodiment reports the cadence, stride length,
foot clearance, pitch angle at the time of toe off, pitch angle at
the time of heel strike, the lateral step position, and the
percentage of time spent in each of the phases of the gait cycle.
Both the average and standard deviation of each metric is reported
for each foot and for the differences between the feet. The method
also performs a statistical test on the left and right metrics to
determine if there is a statistically significant difference.
According to one embodiment, and without limitation, the method
uses a paired t-test with a 5% level of significance. Alternative
embodiments include other parametric and nonparametric tests,
including computer intensive methods such as bootstrap. The method
also reports how many steps or sequences of steps were used to
calculate each of the metrics. Other metrics could be easily
computed and added to this list.
3.M. Display of the Results
[0073] It is often useful and instructive in many applications to
visually display many characteristics of gait. Individual metrics
can be plotted versus time. The left and right sides can be plotted
separately or together on the same plot. As a visual guide, the
system may plot characteristics from the left foot in blue and the
right foot in red. Various colors or line types can be used.
[0074] Since the gait is divided into discrete events (i.e., steps)
or sequences of events, there are many other methods of visual
display that can be used to show the characteristics of the
population of events. For example heatmaps (e.g, FIG. 7) in which
the density of a characteristic versus time or as a percentage of
the gait cycle can be displayed as an image with the metric value
on one axis, the percentage or time on the horizontal axis, and a
color map or grayscale axis for the pixel intensity. Overlapping
trajectories can also be displayed with multiple traces of the
position or orientation of the foot. Individual characteristics can
be plotted as scatter plots.
[0075] In plots showing the average characteristics of a metric, a
surrounding shading region can be used to show the variability of
the metric as measured, for example, by a standard deviation,
standard error of the mean, interquartile range, or a confidence
interval. In our reports we usually show a 95% confidence interval
or a standard deviation. Different embodiments implement a
combination of graphical results as shown in the appendix to the
specification.
Alternative Embodiments
[0076] There are several possible improvements to the methods
described in previous sections. For example, the weighted template
error used for template matching can be scaled and calculated such
that the error during a still period is normalized to 1. This makes
it easier to set thresholds that are more tolerant of variations in
gait across subjects. Specifically the template error can be
calculated as
.epsilon. ( n ) = 1 N s k = 1 N s .epsilon. k ( n ) ( 1 )
##EQU00001##
where .epsilon..sub.k is the template error for a particular sensor
or device. In one embodiment, the gyroscope and accelerometer
magnitudes from the IMUs on both feet are used to determine the
total template error resulting in a template error comprised of
four components (N.sub.s=4). Each component is calculated as
.epsilon. k ( n ) = 1 s k 1 m 1 - m 0 + 1 = m 0 m 1 w 2 ( x n - - p
) 2 ( 2 ) ##EQU00002##
where l is an index representing the lag from the current time n of
the signal segment {x.sub.n}|.sub.n=m.sub.0.sup.m.sup.1, p.sub.l is
the average of the detected templates,
p = 1 N t k = 1 N t x n k - ( 3 ) ##EQU00003##
where n.sub.k is the time index of the kth detected template,
w.sub.l is a weighting factor that can be calculated as the inverse
of the standard deviation of the detected templates at lag l,
w 2 = 1 1 N t - 1 k = 1 N t ( x n k - - p ) 2 ( 4 )
##EQU00004##
[0077] The scaling factor s.sub.k is chosen such that when the
sensor is stationary, .epsilon..sub.k(n)=1. For the gyroscopes this
is calculated as
s k = 1 m 1 - m 0 + 1 = m 0 m 1 w 2 ( p ) 2 ( 5 ) ##EQU00005##
and for the accelerometers this is calculated as
s k = 1 m 1 - m 0 + 1 = m 0 m 1 w 2 ( g - p ) 2 ( 6 )
##EQU00006##
where g is the acceleration due to gravity (approximately 9.81).
One with ordinary skill in the art will know that template error
measures like the weighted squared error used above can be used
with fast methods based on the fast Fourier transform. This method
of weighting could also easily be adapted to other similarity
measures such as mean absolute error, median absolute errors, and
measures of correlation.
[0078] While particular embodiments have been described, it is
understood that, after learning the teachings contained in this
disclosure, modifications and generalizations will be apparent to
those skilled in the art without departing from the spirit of the
disclosed embodiments. It is noted that the foregoing embodiments
and examples have been provided merely for the purpose of
explanation and are in no way to be construed as limiting. While
the methods and apparatuses have been described with reference to
various embodiments, it is understood that the words used herein
are words of description and illustration, rather than words of
limitation. Further, although the methods and apparatuses have been
described herein with reference to particular means, materials and
embodiments, the actual embodiments are not intended to be limited
to the particulars disclosed herein; rather, the methods and
apparatuses extend to all functionally equivalent structures,
methods and uses, such as are within the scope of the appended
claims. Those skilled in the art, having the benefit of the
teachings of this specification, may effect numerous modifications
thereto and changes may be made without departing from the scope
and spirit of the disclosed embodiments in its aspects.
* * * * *