Authors: Che-Chang Yang, Yeh-Liang Hsu, Kao-Shang Shih,
Jun-Ming Lu (2011-04-24); recommended: Yeh-Liang Hsu (2011-11-11).
Note: This paper is published in Sensors,
Vol. 11, No. 8, pp. 7314-7326, 2011/07.
Real-Time Gait Cycle Parameters Recognition Using a Wearable
This paper presents the development of a wearable accelerometry
system for real-time
parameters recognition. Using a tri-axial accelerometer, the wearable motion detector is
a single waist-mounted device to
measure trunk accelerations during walking.
Several gait cycle
parameters, including cadence,
step regularity, stride
regularity and step
can be estimated in
real-time by using autocorrelation procedure. For
validation purposes, 5 Parkinson’s disease (PD) patients and 5 young healthy adults
were recruited in an experiment. The gait cycle parameters among the two
subject groups of different mobility can be quantified and distinguished by the
system. Practical considerations and limitations for
implementing the autocorrelation procedure in such
a real-time system are also discussed. This study can be extended to the
future attempts in real-time detection of disabling gaits, such as festinating
or freezing of gait in PD patients. Ambulatory
rehabilitation, gait assessment and personal telecare for people with gait
disorders are also possible applications.
accelerometer, Parkinson’s disease, gait, mobility
reflect one’s mobility which can be affected by physical impairment, age
progress and changes in health status. Ambulatory gait parameters can be
important measures to assess functional ability, balance control and to predict
fall risk. Individuals with degenerative mobility, e.g., Parkinson’s disease
(PD) patients or older adults usually have gait disorders such as reduced
walking speeds with increased cadences, reduced step/stride lengths, and
increased inter-stride variability . PD patients of advanced stage might
have encountered episodic gait disturbances, like festinating or even freezing
of gaits (FOG) that could lead to falling and adverse health outcomes [2, 3]. Regularity,
rhythm and symmetry are important gait cycle parameters that can be apparently
altered in walking patterns among people of varied mobility [2-4]. Therefore,
the monitoring of the above gait cycle parameters can be beneficial to assess
the mobility and risk of occurrence of episodic gait disturbances.
is frequently based on observational interpretations which are subjective and
may vary among clinicians or investigators. As a consequence, gait monitoring
and analysis techniques have been widely developed and studied. Gait dynamics
can be accurately measured by using optical motion capture systems which
utilize high-speed infrared cameras to record the three-dimensional positions
of retro-reflective markers attached to the joints and segments of the human
body . Gait detection techniques utilizing pressure sensors embedded in an
overground walkway  have also been used. These techniques can detect foot
contact (heel strike and toe-off) and even the foot pressure distribution to
investigate temporal gait parameters. However, those systems are expensive, and
the sophisticated instrumentation requires specialized personnel. Therefore the
uses of those systems are usually limited in laboratory or clinical
environments. Simpler systems based on pressure detection, such as the portable
in-shoe pressure measurement system have also been presented [7, 8]. The
systems utilizing in-shoe pressure detection can only provide simple temporal
gait measures while accelerometer-based or video-based systems can provide
temporal and spatial gait measures, even accurate measurement of lower limbs
and body movement.
using wearable systems has drawn a vast amount of research interests in the
study of human movement. Accelerometers have widely been used in wearable
systems for movement classification, fall detection, estimation of energy
expenditure and gait analysis [9, 10]. Accelerometers in combination with
gyroscopes that measure angular velocity and accurate orientation have also
been developed [11, 12]. Though the pathological gaits have been well studied
and described, only a few studies have investigated recognition of abnormal
gaits using wearable accelerometry systems. A shank-mounted accelerometer was
used to monitor the FOG in PD patients by means of a “freeze index” computed by
frequency spectral analysis. However, the power spectral analysis can hardly be
performed in real-time on compact wearable systems . A wearable system
using ARM7 processor was also demonstrated to detect FOG in real-time from
every collected 0.32s acceleration data . Due to the computation
constraints, it was reported that a longer sample data will produce longer
latency of the system which might not be acceptable for practical uses.
of cost-effective approaches to real-time gait monitoring is important and
paper presents the development of a wearable accelerometry system for
real-time gait cycle parameters recognition. A
waist-mounted wearable motion detector was designed to measure trunk
accelerations during walking. The autocorrelation procedure is implemented in
the system to derive several gait cycle parameters, including cadence, step
regularity, stride regularly and step symmetry. For validation purposes, 5 PD patients and 5 young healthy adults were recruited in order to investigate whether the gait cycle
parameters among the two subject groups of different mobility can be quantified
and distinguished by the proposed system. Practical considerations and
limitations for implementing real-time gait cycle parameters recognition in an
embedded wearable system are also discussed. If the gait cycle parameters can
be identified in real time, continuous detection of gait variability would be
possible. This paper can lead to a future development of wearable systems
enabling real-time recognition of abnormal gaits, such as shuffling,
festinating, or freeze of gait in PD patients, by examining patterns of various
gait cycle parameters and other possible characteristics in acceleration
patterns. Ambulatory rehabilitation, gait assessment and personal telecare for
people with gait disorders are also possible applications.
motion detector is a single waist-mounted device that measures trunk
accelerations of human movements. Figure 1 shows the circuit board assembly and
the prototype of the wearable motion detector. It uses a tri-axial
accelerometer module (KXPA4-2050, Kionix) that senses accelerations in the
sensitivity of 660mV/g over the selected range of ±2g. The accelerometer module
has an internal built-in low pass filter at cut-off frequency of 50Hz. This
circuit limits the bandwidth of the signal outputs and therefore reduces the
higher frequency components which are not related to actual human movements. A
PIC microcontroller (PIC18LF6722, Microchip) offers flash memory of 128kbytes
and SRAM of 3936bytes. It samples the analog output signals via a 10-bit A/D
conversion at the sampling rate of 50Hz. Real-time signal processing can be
implemented in the PIC microcontroller. The wearable motion detector also uses
a wireless 2.4GHz ZigBee RF module (XBee 2.0, Digi International) to transmit
real-time recognized gait cycle parameters to a personal computer (PC). The
gait cycle parameters can be displayed on the PC screen for telemonitoring and
data logging. Powered by 3 AAA batteries (DC4.5V), the wearable motion detector
measures 90mm×50mm×25mm in size and 120g in weight. The battery life can last
up to approximately 50 hours when the wearable motion detector is continuously
motion detector was clipped to the pant belts of the subjects. The detector is
positioned near the middle between the anterior superior iliac spine and the right
iliac crest around the pant belt. The pant belt on the subject was fastened
medium to tight without causing discomfort to the subject, as this adjustment
can reduce misalignment of orientation and vibration of the instrument which
will generate signal artifacts and noises.
Figure 1. The prototype of the wearable motion
2.2. Subjects and Gait Data Collection
purposes, 5 elder Parkinson’s disease patients (4 males and 1 female, 78±9.8
yr) diagnosed as Hoehn & Yahr (H&Y) stage II to III and 5 young healthy
subjects without mobility impairment (all males, 26±3.1 yr) were recruited for
gait data collection. The data collection was approved by the Institutional
Review Board (IRB) at the Far-Eastern Memorial Hospital, Taiwan. The recruited subjects
were provided with necessary information about the measurement and they gave
their informed consents before the data collection.
Before gait data
measurement, the Timed Up and Go (TUG) test, which is a validated simple and
quick measure for mobility assessment , was conducted to quickly screen the
mobility level of all the subjects. The time for the PD patients to perform the
TUG test is 23.9±7.9s, while the young healthy subjects took only 10.6±2.2s.
The distinct difference in the TUG test results show a generally degenerative
mobility level in the PD patient group.
test (5WMT) was conducted in a laboratory. In the 5MWT, the subjects wore the
wearable motion detector at their waists while walking on a 5-meter level
walkway three times at their own normal and faster walking paces. The
accelerations along the vertical (VT), antero-posterior (AP) and medio-lateral
(ML) directions were recorded at the sampling rate of 50Hz. The initiation of
data sampling of the wearable motion detector triggered the start of synchronized video recording during the test for gait
observation and cadence validation.
2.3. Gait Cycle Parameters Recognition
Walking can be
generally regarded as a repeated movement of human body. Therefore the measured
accelerations during walking should also reveal periodic signal patterns. The
autocorrelation procedure is a method to estimate the repeating characteristics
over a signal sequence containing periodic patterns and irregular noises.
Moe-Nilsson et al. have demonstrated the fundamentals of the autocorrelation
procedure for computing gait cycle parameters, which is the basis of the
recognition method in this study .
In the work by
Moe-Nilsson et al., and the subsequent work by Yang et al.  and Keenan et
al.  using autocorrelation for gait cycle analysis, the gait cycle
parameters were computed in an off-line manner. In this paper, the
autocorrelation procedure is implemented in an embedded wearable system for
real-time gait cycle parameters recognition, which extends its possible
applications. The autocorrelation procedure for computing gait cycle parameters
is described below. Practical considerations and limitations for implementing
the autocorrelation procedure in the embedded system for real time gait cycle
parameters recognition are also discussed.
time-discrete acceleration sequence containing N signal points , Equation (1) calculates the autocorrelation coefficient, which is the sum of the products of multiplied by
another signal at the given
phase shift m. The phase shift m can be either positive or negative integers
from 0 to, or from 0 to. Therefore, from an N-point acceleration sequence, its
autocorrelation sequence can be
represented by autocorrelation
coefficients obtained at every phase shift m. The autocorrelation sequence can
either be “biased” or “unbiased”. The unbiased autocorrelation sequence as
shown in Equation (2) is preferred because the biased method generates
noticeable attenuation of coefficient values next to the zero phase shift from
a limited number of data.
segments from to and from to in an
autocorrelation sequence are symmetric with its zero phase shift located at the
center of the sequence. Normalized to 1 at the zero phase shift , only the right half segment to of the
autocorrelation sequence is considered for simplicity. Figure 2 depicts an
example of an autocorrelation sequence computed from the VT accelerations
measured at waist during normal walking paces. The first coefficient peak next to the zero phase shift indicates the first dominant
period, and the second peak the second
dominant period. The peaks and can be detected
by a simple derivative-based method and zero-crossing identification, which are
commonly used in detecting peaks in physiologic signals, such as PQST points in
ECG signals. The two peaks can also be found on the autocorrelation sequence
computed from the AP acceleration sequence. From repeated observations, the two
peaks on the VT autocorrelation sequence appear in the same positions as the
peaks on the AP autocorrelation sequence. Therefore superimposing the VT and AP
autocorrelation sequences can better highlight the exact positions of the two
Figure 2. The example
of an autocorrelation sequence computed from the vertical acceleration measured
at waist during walking.
The following gait cycle parameters can be
derived from the autocorrelation sequence:
Step regularity and stride regularity:
A signal sequence with perfectly repetitive pattern produces its
autocorrelation sequence containing the peak magnitudes identical to its zero
phase shift at every dominant period, i.e., the magnitudes at every dominant
period is 1 for a normalized autocorrelation sequence. The magnitude represents step
regularity, as defined by Moe-Nilssen et al. . This is because the first
dominant period indicates the maximal similarity between the acceleration
sequence and its m-point shifted duplicate.
The m-point span approximates the
duration of a step. Similarly, the second dominant period indicates the maximal
similarity between the acceleration sequence and its 2-step shifted sequence,
and therefore the magnitude represents stride
regularity . Note that the first and second dominant periods do not
represent which of the steps (left-leg or right-leg) as there is no such information
given in the autocorrelation procedure.
Step symmetry: Step symmetry is defined as the
ratio of step regularity to stride regularity, or that indicates
the symmetry between two steps of both legs . In this paper, the step
symmetry is if, and when. Note that this definition is altered from the definition
originally given by Moe-Nilssen et al.  and its modified version by Yang et
al. . In this definition, the step symmetry always ranges from 0 to 1,
which would be more interpretable.
Cadence: Cadence is the step rate per
minute. Let S be the number of steps
taken over the time period t (in
second). Cadence can thus be expressed as Equation (3). The number of steps S can be expressed as the number of the
total samples N divided by the number
of the coefficients n between the
zero phase shift and the first dominant period, i.e., . The time period t during walking can also be
alternatively expressed as N divided
by the sampling frequency f, i.e., . As a result, cadence (c) can be estimated by Equation (4), which
was given by Moe-Nilssen et al. .
sliding window technique is used to cyclically produce gait cycle parameters in
real time from the VT, AP, and ML accelerations. Longer window lengths can
produce more precise gait cycle parameters because the data of more steps is
included. However, considering the real-time processing constraints, longer window
lengths will cause longer computation latency and less reserved margin of
memory capacity. Taking the hardware memory capacity and computation latency of
the wearable motion detector into account, the window length is set at constant
3.5s. According to the assumed average cadence of 105
steps/min that approximates regular walking cadence 75-135steps/min [19, 20],
the choice of 3.5-second data can includes approximately 6 steps, which should
be sufficient for autocorrelation processing.
3. Results and Discussion
3.1 Results of the Experiment
To derive the gait cycle parameters, the VT
acceleration was first used to compute the autocorrelation sequence because the
VT component can represent the characteristics of each
classification of walking . In this study, the autocorrelation sequences computed
from the healthy
young subjects exhibit a more smooth and monotonic
pattern, while the
counterparts obtained from the PD patients contain visible fluctuations and are less
regular. Figure 3 shows one example of such observations in this study. The peak
magnitudes at the
dominant periods from a PD patient’s pattern are relatively lower than that
from a healthy young subject’s pattern.
The example of autocorrelation sequences (VT acceleration) computed from a young healthy subject (above) and
a PD patient (below)
4 shows the autocorrelation sequences computed from VT and AP accelerations obtained
from a healthy subject and a PD patient. The young healthy subject has better
step and stride regularity (larger magnitudes at the peaks D1 and D2)
than the PD patient. This shows that the periodic characteristics of gait in
the PD patient are not steadily and perfectly reproduced. It is shown that the
dominant periods on the VT and AP patterns coincide with each other, even though
the two patterns may vary differently. The comparison of both the VT and AP
autocorrelation sequences can improve the accuracy in identifying the dominant
periods when the dominant periods cannot be clearly determined from the VT
autocorrelation sequence alone.
The example of the VT and AP autocorrelation sequences
computed from a young healthy subject (above) and a PD patient (below)
1 shows the mean values of the recognized gait cycle parameters of all subjects
of the two groups. Comparing the cadences derived from the autocorrelation
procedure, the average cadence of the PD group (102.2±15.2 steps/min)
is slightly higher than that of the healthy group (98.6±5.8 steps/min).
The elevated cadences at longer TUG time for the PD patient group indicate
reduced step length and walking speed, which conforms to the literature result
. In fast 5MWT, the average cadence of the PD group was 108.1±15.6
steps/min., which was approximately 5.8% increased from their normal cadences.
The healthy group had an average cadence of 113.9±6.2 steps/min. in fast 5MWT,
which was approximately 16.9% increased from their normal cadence. This
indicates a limited performance margin for the PD group due to their
For the gait regularity and symmetry, the PD patient
group has the step
regularity of 0.39±0.16 and the stride regularity of 0.43±0.2 during normal
walking paces. The healthy group has higher step regularity (0.61±0.14) and
stride regularity (0.79±0.09). Similar trends can be observed in their fast
walking paces. Therefore, the result shows that the PD patients cannot
well regulate their
repeating steps and
strides compared with the healthy group. Note that the step symmetry
during normal walking from the PD group is slightly higher than that from the healthy group, while the step
symmetry during fast
walking in the PD group is lower than that in the healthy group. This mixed
results regarding step symmetry need further investigations.
In the current definition of step symmetry, it is possible to obtain higher
step symmetry from the gaits with both low step regularity and stride
regularity that are close to each other. Altered definitions of step symmetry
have been used , and it is also of important interest in the future to
investigate which definition can be suitably applicable.
cadence obtained by the method was compared with that measured from the
synchronized video. The mean absolute percentage error is 4.89%. As the
subjects repeated each test item three times during the data collection, it is hypothesized
that the subjects repeated the tests without significant variance so that the
results of gait cycle parameters recognition can be valid. Therefore, the one-way
ANOVA test was used to investigate whether each test item shows significant
variance in both the subject group. In general, there is no significant
difference (significant level 0.05) between the results from each test of the
subjects. Note that because some of the PD subjects were unable to perform fast
5MWT, the test in the PD subjects’ fast 5MWT are excluded in Table 1 due to the
limited data samples.
Table 1. Gait cycle parameters derived from the
studies have reported the use of the vertical accelerations for autocorrelation
procedure [16, 17]. In this study the VT, AP and ML acceleration components were
compared to examine which axis is most sensitive to steps and produces
identifiable pattern related to the gait cycle parameters. From our observation
from the autocorrelation sequences of the 10 test subjects, the ML acceleration
component is considered least descriptive and least sensitive to walking
movement. This was also observed in a previous study by the authors . In
the study by Keenan et al. , the VT and AP components were also used for
autocorrelation process for the same reason even triaxial accelerations were
measured. Though the ML acceleration component measured from the back over the
L3 region has been shown good results in autocorrelation procedure , this
could be because of different positions to attach the devices were used. In the
future developments, the
ML accelerations may be used to distinguish left‐leg
or right‐leg steps . The real-time 3D orientation of the trunk can be
synchronously computed from the vertical acceleration, or from the combination
of the three acceleration components for better accuracy. However, trunk
orientation reveals less information in interpreting gait patterns, and thus it
is not used throughout the recognition procedure.
wearable motion detector developed in this study measures walking movement at
the sampling rate of fs=50Hz. As a
low-pass filter circuit (fc=50Hz) was
internally built in the accelerometer module to reduce the signal components of
higher frequencies that could not be relevant to human movements in daily
living, ideally the sampling frequency above 100Hz should be better. However,
the sampling rate fs=50Hz here is used
because of the limitations of available memory capacity (data to be buffered), computation
capability of the low-cost PIC microcontroller, and the constraint of a
real-time system requiring minimized cyclic processing time. Moreover, intense
and faster movements could rarely occur in daily living home environments.
Compounding the above considerations, the sampling rate 50Hz was used, though
higher sampling rate is certainly better if capable hardware is available.
Step regularity, stride
regularity and step symmetry obtained from the autocorrelation procedure are
the general temporal
estimates in terms of signal periodic characteristics. Cadence can be easily validated
by comparing the synchronized video recording. Tura et al. have validated the
step and stride regularities to well correlate with the indices obtained from
step and stride duration measured from pressure insoles . Offline analysis using autocorrelation
method usually processes large acceleration data to compute the gait cycle
parameters that indicates its
overall gait performances. Real-time recognition of the gait cycle parameters
using cyclic processing may be affected by a high instantaneous variability. An
extra continuous 25-meter walk data from three healthy young subjects in their constant
walking paces was additionally used to check the effect of using shorter window
lengths to calculate the gait cycle parameters. As shown in Table 2, the gait
cycle parameters obtained from the multiple sliding windows (window length 3.5
seconds) can moderately to highly approximate the gait cycle parameters obtained
from the entire single window. However, for data in varied walking speeds, it
was found that gait cycle parameters obtained from the multiple sliding windows
were sensitive and can reflect the episodic signal variability.
Table 2. Gait cycle parameters derived from
multiple sliding windows and the entire single window
Multiple sliding windows
Mean coefficient of variance (CV)
real-time gait cycle parameters recognition is developed, continuous detection
of disabling gaits could be applicable. Figure 5 shows the process flowchart of
the algorithm. After the data sampling (P1), the median-filtering process (P2)
is applied to the sampled data to eliminate signal spikes that are not related
to human movement. The VT and AP autocorrelation sequences are generated (P3)
from the sampled data. In the process P4 both the VT and AP autocorrelation
sequences are superimposed to obtain a peak-highlighted sequence which is used
for peak detection.. The process P5 locates the positions of the first and
second dominant periods in the VT-AP superimposed sequence. If the process D1 fails
to identify the positions of the first and second dominant periods in the VT-AP
superimposed sequence (D1=No), the procedure returns to the process P1 to
restart next data sampling. If that positions are identifiable (D1=Yes), the
peak search process (P6) finds the first and second dominant periods, D1, and D2 in the VT autocorrelation sequence according to the
peak position located from the VT-AP superimposed sequence. The gait cycle
parameters are then computed in the process P7 and the estimates output is
provided prior to the next new processes from P1.
the available gait cycle parameters in consecutive identification periods, a
knowledge base of gait disorders and the pattern characteristics, i.e.,
shuffling, festinating and freeze of gaits can be integrated in the future to facilitate
the potential capability in real-time and continuous detection of disabling
gaits in PD patients.
Figure 5. The process flowchart of real-time gait
cycle parameters recognition.
paper presents the use of a wearable motion detector for real-time gait cycle
parameters recognition. The wearable motion detector is a single waist-mounted
device that utilizes a tri-axial accelerometer to measure trunk accelerations
during walking. The autocorrelation procedure is used to estimate cadence, step
regularity, stride regularity and step symmetry from the measured trunk
accelerations in real time. In this study, the gait cycle parameters among the
two subject groups of different mobility (PD patients and the young healthy
adults) can be quantified and distinguished by the system.
wearable motion detector has been developed by the authors for real-time
physical activity identification, and its connection to a telecare system has
also been presented . The system developed in this paper can lead to future
research interests and development regarding real-time detection of gaits
disorders in PD patients. Mobility assessment, ambulatory rehabilitation for PD
patients can be the possible applications.
research is supported by the Division of Neurology and Orthopedics in Far
Eastern Memorial Hospital, Taiwan. This support is gratefully acknowledged.
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