Authors: Che-Chang Yang, Yeh-Liang Hsu, Kao-Shang Shih, Jun-Ming Lu, Lung
Chan(2011-03-17); recommended: Yeh-Liang Hsu (2011-06-05)
Note：This paper is presented in the
International Conference on System Science and Engineering (ICSSE2011), June
8-10, 2011, Macao.
Real-time gait cycle parameters recognition
using a wearable motion detector
presents the use of an accelerometry-based wearable motion detector for
real-time recognizing gait cycle parameters of Parkinson’s disease (PD) patients.
The wearable motion detector uses a tri-axial accelerometer to measure trunk
accelerations during walking. By using the autocorrelation procedure, several
gait cycle parameters including cadence, gait regularity, and symmetry can be
derived in real-time from the measured trunk acceleration data. The gait cycle
parameters derived from 5 elder PD patients and 5 young healthy subjects are
also compared. The measures of the gait cycle parameters between the PD
patients and the healthy subjects are distinct and therefore can be quantified
and distinguished, which indicates that detection of abnormal gaits of PD
patients in real-time is also possible. The wearable motion detector developed
in this paper is a practical system that enables quantitative and objective
mobility assessment. The possible applications of this system are also
accelerometer, Parkinson’s disease, gait, mobility
reflect one’s mobility which can be affected by physical impairment, age
progress and changes in health status. Gait parameters extracted from complex
ambulation dynamics can be important measures to assess functional ability,
balance control and to predict risk of falling. Parkinson’s disease (PD)
patients suffer from progressive motor disorders, including resting tremor,
bradykinesia, rigidity, and postural instability. The Unified Parkinson’s
Disease Rating Scale (UPDRS) [Fahn et al., 1987] and Hoehn and Yahr (H&Y)
Modified Scale [Hoehn et al., 1967, Goetz et al., 2004] are the two major
clinical measures to assess the PD stages. In addition, the Timed Up-and-Go
test (TUG) [Podsiadlo et al., 1991] and the Berg Balance Scale (BBS) [Berg et
al., 1989] are also the two assessment tools in terms of mobility.
PD affects gait
disorders such as reduced walking speed with increased cadence, reduced
step-length, and increased stride-to-stride variability [Lowry et al., 2008].
Shuffling gait is also commonly observed from moderate PD patients. The
advanced PD patients may have experienced the episodic gait disorders, such as
festination, hesitation and freeze of gait (FOG) that occur occasionally and
intermittently and may lead to falling and adverse health outcomes (e.g., hip
fracture) [Hausdorff, 2009].
is frequently based on observational interpretations which are subjective and
may vary among clinicians or investigators. As a consequence, monitoring and
analysis techniques for the Parkinsonian and pathological gaits have been
widely developed and studied. Gait dynamics can be accurately measured by using
the 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 during motion [Melo-Roiz et al., 2010].
Gait detection techniques utilizing pressure sensors embedded in an overground
walkway [Menz et al., 2004] or a portable in-shoe pressure measurement system
have also been used [Femery et al., 2004]. These techniques detect foot contact
(heel strike and toe-off) and even the foot pressure distribution to
investigate spatial-temporal gait parameters. However, those systems are
expensive, and the sophisticated instrumentation requires specialized
personnel. Therefore the uses of those systems are only limited in laboratories
or clinical environments.
using wearable systems has drawn a vast amount of research interests in the
study of human movement. It is only recently that a few numbers of studies have
reported gait analysis using accelerometers while the accelerometer-based
trials in movement classification, estimation of energy expenditure and fall
detection have been largely studied [Mathie et al., 2004, Yang et al., 2010].
Though the Parkinsonian gaits have been well studied and described, only a few
studies have investigated recognizing abnormal gaits using wearable systems. A
shank-mounted accelerometer was used to monitor the FOG of the PD patients. A
frequency spectra analysis was used to compute the frequency components of gait
data. A freeze index is defined as the ratio of two spectral bands of different
frequency components 0.5-3Hz and 3-8Hz. However, the power spectral analysis
cannot be performed in real-time on compact wearable systems [Moore et al.,
2008]. A wearable system using ARM7 processor was also demonstrated to detect
FOG in real-time from every collected 0.32s acceleration data [Jovanov et al.,
2009]. 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.
presents the use of the wearable motion detector for real-time recognizing gait
cycle parameters of PD patients. A waist-mounted wearable motion detector was
designed to measure trunk accelerations during walking. The autocorrelation
procedure was applied to derive several gait cycle parameters, including
cadence, step regularity, stride regularly and step symmetry. Five PD patients
and five young healthy subjects were recruited in a data collection session.
The differences in the gait cycle parameters between PD patients and the
healthy subjects were compared and discussed. The study in this chapter can
lead to a future development of a wearable system for recognizing abnormal
gaits, such as shuffling, festinating, or freeze of gait and falls in PD
patients in real-time, which can be important and beneficial in PD ambulatory
rehabilitation and personal tele-care applications.
motion detector is a single waist-mounted device that measures trunk
accelerations of human movements. Figure 1 shows the circuit board 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 output of the accelerometer module
is firstly low-pass filtered at the cut-off frequency of 50Hz to reduce signal
noises. A PIC microcontroller (PIC18LF6722, Microchip) 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 ZigBee RF module (XBee 2.0, Digi
International) which enables wireless data transmission to a PC via a 2.4GHz
ZigBee protocol. Powered by 3 AAA batteries (DC4.5V), the wearable motion
detector measures 90mm×50mm×25mm in size and weights 120g.
Figure 1. The prototype of the wearable motion
2.2 Subjects and gait data collection
In order to
compare the gait cycle parameters between healthy subjects and PD patients, 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 (all
males, 26±3.1 yr) were recruited for the gait data collection. The test was
approved by the Institutional Review Board (IRB) at the Far-Eastern Memorial
Hospital, Taiwan. The subjects were provided with necessary information about
the test and they gave their informed consent before the test.
collection included the TUG test and the 5-meter-walk test (5WMT) conducted in
a laboratory. The TUG test is a validated simple measure to quickly screen
mobility of individuals. The subjects were firstly directed to perform the TUG
test. In the TUG tests, the subjects were asked to stand up from a seated
posture on a chair, then walk forward for 3 meters, turn 180 degrees and walk
back 3 meters, then turn 180 degrees and finally sit down on a chair. The time
taken to complete the sequential tasks by each subject was measured.
In the 5MWT, the
subjects wore the wearable motion detector at their waists while walking on a
5-meter level walkway at their normal and fast walking paces, respectively. The
accelerations along the vertical (VT), antero-posterior (AP) and medio-lateral
(ML) directions were recorded at the sampling rate of 50Hz. Synchronized video
recording was also taken to observe the behaviors of the subjects during the
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 [Moe-Nilssen et al., 2004].
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 which range 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”. As shown in Equation (2), the unbiased
autocorrelation sequence is preferred because the biased method generates
noticeable attenuation of coefficient values next to the zero phase shift from
a limited number of data [Moe-Nilssen et al., 2004].
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 , the right half segment to of the
autocorrelation sequence is only
considered for simplicity. Figure 2 depicts an example of an autocorrelation
sequence computed from 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
Figure 2. The example of an autocorrelation
sequence computed from the vertical acceleration measured at waist during
gait cycle parameters can be derived from the autocorrelation sequence:
(1) Step regularity and stride regularity
regularity. 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 of can represent
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.
(2) Ste symmetry
of step regularity to stride regularity, or is the step
symmetry that indicates the symmetry between two steps of both two legs.
Cadence (c) can be estimated by Equation (3),
where f is the sampling frequency and n the amount of the coefficients between
the zero phase shift and the first dominant period. This equation estimates
cadence without the need of walking speed or distance.
A sliding window
technique is used to cyclically recognize gait cycle parameters in real-time.
Take the hardware memory capacity and computation latency of the wearable
motion detector into account, the window length is set 3.5s. According to the
assumed average cadence of 104 steps per minute, the choice of 3.5-second data
can includes approximately 6 steps that should be considered sufficient for
real-time processing [Hirasaki et al., 1999].
Result and Discussion
To derive the
gait cycle parameters, the VT acceleration was used to compute the
autocorrelation sequence because the VT component may better carries the
characteristics of steps during walking. In this study it is observed that the
autocorrelation sequences from the healthy young subjects are more smooth and
monotonic, while the counterparts from the PD patients contain subtle
fluctuations and are less regular. Figure 3 shows one example of such
observations in this study. The magnitudes at the dominant periods from a PD
patient’s pattern are relatively lower than that from a healthy young subject’s
pattern. Accordingly it implies less regular movements between each step, which
can be considered the results from ill-controlled motor behaviors. This
requires more PD data to justify this observation.
Figure 4 shows
the autocorrelation sequences computed from VT and AP accelerations of a
healthy subject and a PD patient. It is shown that the dominant periods on the
VT and AP patterns can coincide with each other, though the two patterns vary
differently. The comparison of both the VT and AP autocorrelation sequences is
helpful in precisely identifying the dominant periods when the dominant periods
cannot be clearly determined on the VT autocorrelation sequence alone.
Figure 3. The example of an autocorrelation
sequence (VT acceleration) computed from a healthy young subject (above) and a
PD patient (below)
Figure 4. The example of the VT and AP
autocorrelation sequences computed from a healthy subject (above) and a PD
Table 1 shows
the statistical results of the gait cycle parameters from the 10 test subjects.
The average values and its standard deviation are shown. The clinical
assessment method TUG Test performance measured in the healthy subject group
was 10.6±2.2s while a longer time 23.9±7.9s was measured in the PD patient group.
This simple estimate shows a degenerative mobility of the PD patient group.
cadences derived from the autocorrelation procedure, the PD group had the
cadence slightly higher than the healthy group. The cadence of the PD group in
fast 5MWT was 108.1±15.6 steps/min., which was approximately 5.8% increased
from their normal cadences. The healthy group had the 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 degenerative mobility.
Table 1. Gait cycle parameters of the subjects
Samples of a
Samples of a
regularity and symmetry of gaits, the PD 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 PD patients have less regular performance in repeating steps and
strides compared with the healthy group. Note that the symmetry during their
normal walking speed in the PD group is higher than that in the healthy group,
while the symmetry during fast walking in the PD group is lower than that in
the healthy group. This mixed results regarding gait symmetry need further
have reported the use of the vertical accelerations for autocorrelation
procedure [Yang et al., 2010, Moe-Nilssen et al., 2004, Keenan et al., 2005].
In this study the VT, AP and ML accelerations were compared to examine which
axis is most sensitive to steps and produces identifiable pattern related to
the gait cycle parameters. With visual inspection from the autocorrelation
sequences of the 10 test subjects, the ML pattern is considered least
descriptive and least sensitive to walking movement.
presents the development of an accelerometry-based 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 several temporal gait cycle parameters in real-time. Cadence, step
regularity, stride regularity and step symmetry can be derived from the
autocorrelation sequences computed from the measured trunk accelerations.
In this study
the current focus of research interest here is whether those selected gait
cycle parameters can be quantified and distinguishable between PD patients and
healthy people without disability. The PD patient group and the healthy subject
group recruited in this study show varied characteristics in the gait cycle
parameters. The PD patient group with impaired mobility can be found to have
reduced gait regularity and symmetry though some details still needs further
selection of the thresholds for the gait cycle parameters, it is possible that
the algorithm can facilitate real-time recognition of abnormal gaits, like
shuffling, or festinating gaits from PD patents. Integrated with extended
capabilities, such as verbal cueing, movement classification and fall
detection, the future development of this study is expected to provide a
low-cost system that can benefit and assist ambulation rehabilitation for PD
patients and prompt personal tele-care applications.
This research is
supported by the Sections of Neurology and Orthopedics in Far Eastern Memorial
Hospital, Taiwan. This support is gratefully acknowledged.
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