Extraction of Temporal Gait Parameters using a Reduced Number of

Wearable Accelerometers

Mohamed Boutaayamou

1,2

, Vincent Denoël

Olivier Brüls

, Marie Demonceau

, Didier Maquet

Bénédicte Forthomme

, Jean-Louis Croisier

, Cédric Schwartz

, Jacques G. Verly

and Gaëtan Garraux

4,5

Laboratory of Human Motion Analysis, University of Liège (ULg), Liège, Belgium

INTELSIG Laboratory, Department of Electrical Engineering and Computer Science, ULg, Liège, Belgium

Department of Rehabilitation and Movement Sciences, ULg, Liège, Belgium

Movere Group, Cyclotron Research Center, ULg, Liège, Belgium

Department of Neurology, University Hospital Center, Liège, Belgium

Keywords: Gait Analysis, Wearable Accelerometers, Wavelet Analysis, Validation, Gait Segmentation, Gait Events,

Heel-off, Heel Strike, Toe Strike, Toe-off, Heel Clearance, Stance Time, Swing Time, Stride Time.

Abstract: Wearable inertial systems often require many sensing units in order to reach an accurate extraction of

temporal gait parameters. Reconciling easy and fast handling in daily clinical use and accurate extraction of

a substantial number of relevant gait parameters is a challenge. This paper describes the implementation of a

new accelerometer-based method that accurately and precisely detects gait events/parameters from

acceleration signals measured from only two accelerometers attached on the heels of the subject’s usual

shoes. The first step of the proposed method uses a gait segmentation based on the continuous wavelet

transform (CWT) that provides only a rough estimation of motionless periods defining relevant local

acceleration signals. The second step uses the CWT and a novel piecewise-linear fitting technique to

accurately extract, from these local acceleration signals, gait events, each labelled as heel strike (HS), toe

strike (TS), heel-off (HO), toe-off (TO), or heel clearance (HC). A stride-by-stride validation of these

extracted gait events was carried out by comparing the results with reference data provided by a kinematic

3D analysis system (used as gold standard) and a video camera. The temporal accuracy ± precision of the

gait events were for HS: 7.2 ms ± 22.1 ms, TS: 0.7 ms ± 19.0 ms, HO: −3.4 ms ± 27.4 ms, TO:

2.2 ms ± 15.7 ms, and HC: 3.2 ms ± 17.9 ms. In addition, the occurrence times of right/left stance, swing,

and stride phases were estimated with a mean error of −6 ms ± 15 ms, −5 ms ± 17 ms, and −6 ms ± 17 ms,

respectively. The accuracy and precision achieved by the extraction algorithm for healthy subjects, the

simplification of the hardware (through the reduction of the number of accelerometer units required), and

the validation results obtained, convince us that the proposed accelerometer-based system could be extended

for assessing pathological gait (e.g., for patients with Parkinson’s disease).

1 INTRODUCTION

Wearable inertial systems have been proposed to

measure gait events and to estimate temporal gait

parameters (e.g., Willemsen et al. 1990; Aminian et

al., 1999; Selles et al., 2005; Lee et al. 2007;

Godfrey et al., 2008). Compared to conventional gait

analysis techniques, such as optoelectronic motion

capture systems and instrumented walkways, these

systems are not limited to controlled laboratory

environment; they can handle gait analysis in an

entirely natural setting with the possibility to obtain

gait parameters over longer walking distances (e.g.,

Khandelwal and Wickström 2014). The hardware

part of inertial systems, such as accelerometer units,

includes low-cost, small, and lightweight sensing

units with generally low power consumption (e.g.,

Stamatakis et al., 2011). With an appropriate

algorithm, these inertial systems are particularly

suitable for assessing gait in a clinical environment

(e.g., Salarian et al., 2004; Rueterbories et al., 2010).

Yet, these systems often need many sensing units

to achieve reasonable accuracy and precision in the

extraction of gait events/parameters. Arranging these

Boutaayamou, M., Denoël, V., Brüls, O., Demonceau, M., Maquet, D., Forthomme, B., Croisier, J-L., Schwartz, C., Ver ly, J. and Garraux, G.

Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers.

DOI: 10.5220/0005696900570066

In Proceedings of the 9th International Joint Conference on Biomedical Engineer ing Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 57-66

ISBN: 978-989-758-170-0

 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reser ved

sensor units on lower limbs (e.g., feet) in a manner

that is acceptable for clinical gait analysis remains a

challenge.

We have previously developed and validated a

signal processing algorithm to automatically extract

gait events in healthy walking – labelled as heel

strike (HS), toe strike (TS), heel-off (HO), and toe-

off (TO) – from acceleration signals measured by

four accelerometer units attached to heels and toes

(Boutaayamou et al., 2015a). The algorithm

exploited distinctive and remarkable features in

these acceleration signals to identify and extract gait

events with good accuracy and precision.

In this paper, we present a new signal processing

algorithm that extracts the same gait events from

only two accelerometers, i.e., one on each shoe, at

the level of the heel. Our approach to the

aforementioned problem consists therefore in

reducing the number of accelerometer units by

eliminating the two units on the toes. We also extend

the previous algorithm to detect a new gait event,

i.e., the time of heel clearance (HC) which is an

important gait event that can refine the swing phase.

In addition, we consider the validation on a stride-

by-stride basis of the proposed algorithm on a group

of healthy people during normal walking. In this

validation, we compare the results (i.e., measured

gait events and calculated temporal gait parameters

of interest) to reference data provided by a kinematic

3D system (used as gold standard) and a video

camera.

2 METHOD

2.1 Wearable Accelerometer System

Acceleration signals during walking were recorded

by a wearable, wireless accelerometer-based

hardware system which includes several small three-

axis accelerometer units (2 cm x 1 cm x 0.5 cm), a

transmitter module, and a receiver module

(Stamatakis et al., 2011; Boutaayamou et al., 2015a)

(Figure 1). This system can measure accelerations

up to ±12 g (where g = 9.81 m/s² is value of the

gravitational acceleration) along its three sensitive

axes: x (horizontal), y (transverse), and z (vertical).

In this study, two accelerometer units were tightly

attached on the right and left feet, i.e., one on each

shoe at the level of the heel. The right and left

accelerometers were synchronized. Accelerometers

were connected to the transmitter module positioned

on the waist. The wires between accelerometers and

(a) (b)

Figure 1: (a) The wearable accelerometer-based hardware

system. (b) Schematic illustration of the placement of a

wearable accelerometer (either for right or left foot) and

the direction of axes.

the transmitter module were tightly strapped around

the legs so as to avoid disturbing the subject

movements. Acceleration signals were recorded at

200 Hz. All data were analyzed using Matlab 7.6.0

(MathWorks, Natick, MA, USA).

2.2 Subjects and Gait Tests Procedure

Gait signals were recorded during walking tests

performed by seven young and healthy subjects

without any previous injury of the lower limbs

((mean ± standard deviation) age = 27 ± 2.6 years;

height = 181 ± 7 cm; weight = 78 ± 9 kg). All of

them provided informed consent. The gait tests

procedure of this study is similar to the one reported

in (Boutaayamou et al., 2015a). Before we started

the measurements, subjects took sufficient time to

get used to the instrumentation tools and the

experimental procedure. During the tests, they were

asked to walk on a 12–m long track, at their

preferred, self-selected, usual speed. Each subject

performed several gait tests of 60 s. Subjects wore

their own regular shoes. All of the walking tests

were performed at the Laboratory of Human Motion

Analysis of the University of Liège, Belgium.

2.3 Wavelet Analysis: Segmentation of

Acceleration Signals

In the present study, we use a segmentation method

that we have previously developed (Boutaayamou et

al., 2015b) to identify gait patterns from only heel

acceleration signals, thereby reducing the number of

wearable accelerometers and allowing for a robust

extraction of the gait events/parameters (see Sec.

2.4). This segmentation method is based on the

continuous wavelet transform (CWT) to isolate (1)

time intervals where the heel acceleration is close to

zero, from (2) time intervals the accelerometers are

moving. The wavelet coefficient C(a,b) of the CWT

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

Figure 2: Rough estimation of heel flat/non-flat phases

using the gait segmentation based on the continuous

wavelet transform.

of a signal s(t) is defined as

(,)=







(



)



∗







−



,

(1)

where a (≠0) and b (∈ℝ) are the scale and location

parameters, respectively, 

∗

is the complex

conjugate of the mother wavelet function , and t is

the time. Compressing a (small values of a) tracks

high frequencies changes whereas stretching a (large

values of a) tracks low frequencies. C(a,b) thus

measures the similarity between the signal s(t) and

the scaled and shifted versions of , with larger

values indicating higher similarity. The wavelet

(Mexican hat) is chosen here as the mother

wavelet .

The detailed description of the developed

segmentation method is not the focus of this study.

Rather, we consider the results of its application to

the vertical acceleration signal measured at the level

of the heel. We therefore obtain a “heel binary

function” that roughly estimate heel flat phases

(motionless periods) and heel non-flat phases. A

typical result of the binary function is shown in

Figure 2 (dashed lines). The segmentation method

has the advantage that it avoids to look directly for

specific gait events. The segmentation only

determine rough heel flat/non-flat phases in which

gait events of interest can be further extracted with

good accuracy.

2.4 New Signal Processing Algorithm

In order to estimate precisely gait parameters such as

the durations of the stance, swing, and stride phases

during a gait cycle (i.e., the duration of a stride), it is

necessary to detect, for each foot, the precise

moments of gait events of interest during the same

gait cycle. These gait events are characterized by

distinctive and remarkable features on heel

acceleration signals. Depending on the nature of

these features, a suitable method is employed in the

present study to accurately extract gait events. For

clarity, we consider only one foot. It is obvious that

the algorithm could be applied in the same way for

both feet.

Times of occurrence of HS

accel

, TS

accel

, HO

accel

, and HC

accel

are identified mainly from the

acceleration signals in sagittal plane, i.e., with

respect to the x-axis and z-axis accelerations denoted

by z



and x



, respectively. The subscripts accel, ref,

and h refer to our method, to the reference methods,

and to the heel, respectively.

2.4.1 Gait Events Identification

We now describe the main steps of the detection

following the chronological occurrence order of

healthy gait events (i.e., not the order that the

algorithm follows to extract these events).

a) The time of the heel strike event: HS

accel

In the present study, we adapt the method

described in (Boutaayamou et al., 2015a) to detect

accel

as follows:

• At HS

accel

, the heel acceleration signal 



is subject

to abrupt changes (Figure 3a). To detect HS

accel

we only consider the segment defined as the

second half of the heel non-flat phase. In this

segment, HS

accel

is identified using the magnitude

of z



filtered with a 4

-order zero-lag Butterworth

high-pass filter (cutoff frequency=10 Hz). HS

accel

is detected as the time of occurrence of the

maximum value of the magnitude of this filtered





(Figure 3a). As pointed out in (Boutaayamou et

al., 2015a), the determination of HS

accel

is robust

with respect to this filtering step, since HS

accel

occurs rapidly with a frequency larger than 10 Hz.

b) The time of the toe strike event: TS

accel

can be extracted from the heel

acceleration signal as the accelerometer is sensitive

enough to measure the acceleration movement of the

foot when the toe hits the ground. The main steps to

estimate TS

accel

are as follows:

• As TS

accel

occurs after HS

accel

and before HO

accel

we seek TS

accel

in the segment [HS

accel

, 0.4*HS

accel

+0.6* HO

accel

] (the procedure for extracting HO

accel

is explained in c)). TS

accel

is automatically detected

using x



and z



restricted to this segment. The

resulting local signals are then filtered with a 4

order zero-lag Butterworth low-pass filter (cutoff

frequency = 20 Hz), and integrated twice in order

to calculate their associated position signals. The

33.5 34 34.5 35 35.5

−5

Time [s]

Acceleration [g]

Heel flat phases

Heel binary function

˙z˙

Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers

drift related to this double integration is limited

since the latter is performed in a small time

interval. We then apply a piecewise-linear fitting

method to each of these position signals. This

method estimates a location of convex curvature in

a signal using two linear segment that best fit this

signal in the least-square sense (Appendix)

(Boutaayamou et al., 2015a). The times of

resulting convex curvatures in the two position

signals are denoted t

and t

. It is then assumed that

accel

is estimated as the mean of t

and t

(Figure 3b).

c) The time of the heel-off event: HO

accel

is automatically detected in the segment

that lies between 125 ms after HS

accel

and 70 ms

before TO

accel

(the extraction method of TO

accel

described in d)). We adapt the method presented in

(Boutaayamou et al., 2015a) to detect HO

accel

from

z



as follows:

• We consider the local signal obtained from the

restriction of z



to the previous segment. This

local signal is then filtered with a 4

-order zero-

lag Butterworth low-pass filter (cutoff

frequency = 20 Hz). This filtering step does not

alter the physical significance of the local signal

(Boutaayamou et al., 2015a). Since this signal

corresponds to a slow movement (some

milliseconds before and after HO

accel

), there is no

critical peak to be detected that could be removed

erroneously in this filtering step (Boutaayamou et

al., 2015a). A double integration of this local

acceleration signal is then performed to calculate

the corresponding position signal. The drift that

could be generated from this double integration is

negligible since the latter is carried out in a small

time interval. We apply the aforementioned

piecewise-linear fitting method twice to the

resulting local position signal in order to estimate

successive locations of convex curvature in this

local position signal. The time of the last location

of convex curvature is our estimate of HO

accel

(Figure 3c).

d) The time of the toe-off event: TO

accel

• At TO

accel

, the direction of motion of the ankle

joint changes from plantarflexion to dorsiflexion in

the sagittal plane (Whittle, 1996). It is assumed

that TO

accel

corresponds to the time when a zero

crossing of the vertical heel acceleration signal

occurs after the beginning of the non-flat

phase (Figure 3d).

e) The time of the heel clearance event: HC

accel

• HC

accel

is defined as the moment when the

minimum clearance between the heel

accelerometer and the ground is achieved during

the swing phase. We consider distinctive vertical

heel acceleration features that indicate where

accel

can be found in the time and frequency

domains. These features are rather sharp negative

peaks in z



(Figure 3e) involving some mid

frequencies. In order to extract HC

accel

, we apply

the CWT (see Sec. 2.3) to the local signal defined

as the restriction of z



to the neighbourhood of

these features. The CWT is indeed adapted for

identifying HC

accel

because it allows detection of a

specified frequency at a specified time. The

previous local signal is then decomposed into

wavelet packages. The wavelet (Mexican hat) is

used as the mother wavelet to extract HC

accel

as it

is similar to the pattern of the aforementioned

features. A typical result is depicted in Figure 3e.

2.4.2 Extraction of Temporal Gait

Parameters

Temporal gait parameters, such as durations of the

stance, swing, and stride phases, are calculated on

the basis of the previous gait events as follows:

• Right stance duration (time between right HS

(HS

right

) and right TO (TO

right

) during stride i)

Right stance = TO

right

( i ) – HS

right

( i ) .

• Left stance duration (time between left HS (HS

left

)

and left TO (TO

left

) during stride i)

Left stance = TO

left

( i ) – HS

left

( i ) .

• Right swing duration (time between HS

right

stride i+1 and TO

right

of stride i)

Right swing = HS

right

( i+1 ) – TO

right

( i ) .

• Left swing duration (time between HS

left

stride i+1 and TO

left

of stride i)

Left swing = HS

left

( i+1 ) – TO

left

( i ) .

• Right stride duration (time between two

consecutive right HSs)

Right stride = HS

right

( i+1 ) – HS

right

( i ) .

• Left stride duration (time between two consecutive

left HSs)

Left stride = HS

left

( i+1 ) – HS

left

( i ) .

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

(a) (b)

(e)

Figure 3: Vertical heel acceleration signal (i.e., z



measured by our accelerometer system) and reference kinematic signals

(i.e., the vertical heel position z



and the vertical toe position z



measured by the Codamotion system). The gait events, i.e.,

(a) HS, (b) TS, (c) HO, (d) TO, and (e) HC, detected by our method and by reference methods are shown on each signal

during typical consecutive strides.

2.5 Stride-by-stride Validation Method

2.5.1 Reference Data

A kinematic 3D analysis system (Codamotion

system; Charnwood Dynamics; Rothley, UK) and a

video camera provided reference data to validate, on

a stride-by-stride basis, the gait parameters/events

determined by our method.

The kinematic system is based on active optical

technology; it can accurately measure the 3D

positions of active markers placed in the body

locations of interest. We collected kinematic data at

the level of the heel and the toe of each foot at

400 Hz. The heel marker was placed upon the heel

accelerometer. The video camera (30 fps) was

placed close to the track such that the pointing

direction is approximately perpendicular to the

sagittal plan.

Kinematic data were used to validate, on a stride-

by-stride basis, the gait events HS

accel

33.5 34 34.5 35 35.5

Time [s]

Acceleration [g] − Position [x20 cm]

˙z˙

(HS

ref

)

˙z˙

(HS

accel

)

33.5 34 34.5 35 35.5

−4

−2

Time [s]

Acceleration [g] − Position [x20 cm]

˙z˙

(TS

ref

)

˙z˙

(TS

accel

)

33.5 34 34.5 35

Time [s]

Acceleration [g] − Position [x20 cm]

˙z˙

(HO

accel

)

(HO

ref

)

˙z˙

33.5 34 34.5 35 35.5 36

−5

Time [s]

Acceleration [g] − Position [x20 cm]

˙z˙

(TO

accel

)

(TO

ref

)

33.5 34 34.5 35 35.5 36

−5

Time [s]

Acceleration [g] − Position [x20 cm]

(HC

ref

)

˙z˙

(HC

accel

)

˙z˙

Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers

Table 1: The results of our method are compared to the results of reference methods considering several consecutive strides.

This evaluation is given as the accuracy (mean of the differences), the precision (std. dev. of the differences), limits of

agreement, 95% confidence interval (CI) of the differences, and 95% CI of the lower and upper limits of agreement.

Accuracy (ms)

(precision (ms))

Limits of

agreements (ms)

95% CI of the

differences (ms)

95% CI of the

lower limits (ms)

95% CI of the

upper limits (ms)

No. of

events

7.2(22.1) −36.250.7]  5.6 8.8] −38.9 −33.5] 47.953.4]

771

0.7(19.0)

−36.638.0] −0.9 2.3] −39.3 −33.9] 35.340.7] 567

−3.4(27.4)

−57.250.3] −8.2 1.3] −66.5 −49.9] 43.159.7] 126

2.2(15.7) −28.633.0]  1.1 3.3] −30.5 −26.8] 31.234.9]

819

3.2(17.9)

−31.938.3]  1.9 4.4] −34.7 −30.5] 36.941.1] 839

accel

and HC

accel

. Reference gait events HS

ref

and

ref

, were obtained by the kinematic method

reported in (Boutaayamou et al., 2014). HS

ref

and

ref

were extracted solely from measured heel and

toe coordinates during overground walking (Figures

3a and 3d). TS

ref

was extracted from the vertical toe

position signal (Boutaayamou et al., 2015a) in each

gait cycle (Figure 3b). HC

ref

was detected as the time

of the local maximum of heel clearance (Figure 3e).

The video camera provided HO

ref

2.5.2 Evaluation Method

We evaluated the level of agreement between our

method and the reference methods by quantifying

the accuracy, precision, absolute error, and intraclass

correlation coefficient (ICC). Accuracy and

precision were computed as the mean and standard

deviation (std. dev.), respectively, of the differences

between the gait events for each stride, i.e.,

accel

– HS

ref

, TS

accel

– TS

ref

, HO

accel

– HO

ref

accel

– TO

ref

, and HC

accel

– HC

ref

. The absolute

error was calculated as the mean and std. dev. of

absolute values of the previous differences. The ICC

evaluates the statistical agreement between our

method and the reference methods. A Bland-Altman

analysis was also carried out.

3 RESULTS

Table 1 provides a quantitative one-by-one

comparison of gait events. Because of the limited

number of extracted reference events and the

variation of some reference patterns among subjects,

the sample size for the compared gait events was not

always the same but ranged between 126 and 839

events. During some gait tests, we observed that

some markers – used to record reference kinematic

signals – detached from the shoes. We therefore

excluded the associated gait events from the

analysis. In addition, we emphasize that HO

ref

was

obtained only by the video camera. The extraction of

ref

is thus limited to one stride during a given gait

test. The total number of HO

ref

(here 126) is

therefore much smaller than that of HS

ref

, TS

ref

, and HC

ref

. The four latter reference data were

indeed extracted from consecutive strides.

The accuracy and precision of gait events

detection ranged from −3.4 ms to 7.2 ms, and

15.7 ms to 27.4 ms, respectively. Given the

sampling frequency of 200 Hz of the recorded heel

accelerations for both feet, the accuracy and the

precision of detection are less than durations of 2

frames (10 ms) and 6 frames (30 ms), respectively.

Figure 4 shows the Bland-Altman plots of gait

events differences. We observe small systematic

biases in accordance with the accuracy of detection

provided in Table 1. The proposed method tends to

detect earlier gait events except for HO. In addition,

the limits of agreement (i.e., mean ± 1.96 std. dev.)

and their associated 95% confidence interval exhibit

small variations in the times of gait events (Table 1).

Table 2 shows the results of durations of stance,

swing, and stride phases calculated by our method

and by the reference method (i.e., provided by the

Codamotion system) for the right and left feet. These

temporal parameters could be estimated with a mean

absolute error less than 15 ms. The ICC coefficient

was larger than 0.95 for both stance time and stride

time, and larger than 0.87 for swing time.

Figure 5 shows the Bland-Altman plots of the

temporal parameters for the right and left feet. Most

differences of these temporal parameters are within

the 1.96 std. dev. lines.

4 DISCUSSION

We have presented a new signal processing

algorithm that extracts relevant temporal gait

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

(a) (b)

(e)

Figure 4: Bland−Altman plots of the gait events, i.e., (a) HS, (b) TS, (c) HO, (d) TO, and (e) HC, measured using our

method and reference methods, with mean (dash-dotted line in the middle) of differences HS

accel

– HS

ref

, TS

accel

– TS

ref

accel

– HO

ref

, TO

accel

– TO

ref

, and HC

accel

– HC

ref

. 95% of these differences are between the lines ± 1.96 std. dev. (dashed

lines). (o) and (

+) refer to gait events measured at the right foot and those measured at the left foot, respectively.

parameters/events from only two accelerometers

attached to the right and left feet, i.e., one on each

shoe at the level of the heel.

The new algorithm is versatile enough to detect

gait events. The algorithm is based on the CWT and

an original piecewise-linear fitting method. Those

methods allow for an automatic and robust

extraction of gait events from relevant local

acceleration signals. The algorithm was validated by

comparing results obtained by our method to those

obtained by a kinematic 3D system (used as gold

standard) and a video camera. The experimental

results show a good agreement between our

algorithm and the reference, and demonstrate an

accurate and precise detection of HS, TS, HO, TO,

and HC in a group of healthy people during normal

walking. In addition, the algorithm computes the

time of stance, swing, and stride phases with a good

10 15 20 25 30 35 40 45 50 55 60

−80

−60

−40

−20

(

accel

+HS

[

]

accel

−HS

ref

[ms]

10 15 20 25 30 35 40 45 50 55 60

−80

−60

−40

−20

(TS

accel

+TS

)/2[s]

accel

−TS

ref

[ms]

10 15 20 25 30 35 40 45 50 55 60

−80

−60

−40

−20

(HO

accel

+HO

)/2[s]

accel

−HO

ref

[ms]

10 15 20 25 30 35 40 45 50 55 60

−80

−60

−40

−20

(TO

accel

+TO

)/2[s]

accel

−TO

ref

[ms]

10 15 20 25 30 35 40 45 50 55 60

−80

−60

−40

−20

(HC

accel

+HC

)/2[s]

accel

−HC

ref

[ms]

Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers

Table 2: Results of right/left stance, swing, and stride phase durations calculated by our method are compared to those

obtained by a reference kinematic system, Codamotion, used as gold standard. This comparison is given as the difference of

the estimated values (mean error), the mean of the absolute error (abs. error), and the intraclass correlation

coefficient (ICC).

Gait

parameters

Foot Accelerometers Codamotion Mean error Abs. error ICC No. of

strides

Stance time (s)

Right

0.6700.047 0.674 0.055 −0.0040.016 0.0120.011

0.95 188

Left

0.6560.052 0.662 0.055 −0.0060.015 0.0120.010

0.95 220

Right

and left

0.6620.050 0.668 0.055 −0.0060.015 0.0120.010

0.95 408

Swing time (s)

Right

0.4040.042 0.399 0.035 0.0050.018 0.0140.012

0.89 336

Left

0.4180.038 0.413 0.035 0.0050.018 0.0140.011

0.87 383

Right

and left

0.4120.041 0.407 0.035 −0.0050.017 0.0140.011

0.88 719

Stride time (s)

Right

1.0800.092 1.083 0.092 −0.0030.016 0.0120.011

0.98 181

Left

1.0810.090 1.089 0.098 −0.0080.018 0.0150.013

0.98 227

Right

and left

1.0800.090 1.087 0.095 −0.0060.017 0.0130.012

0.98 408

(a) (b)

(c)

Figure 5: Bland−Altman plots of the temporal gait parameters, i.e., (a) stance time, (b) swing time, and (c) stride time

estimated during consecutive strides by our method and the gold standard method. (o) and (

) refer to right and left gait

parameters, respectively.

accuracy and precision.

Previous studies reported results of gait

parameters during the normal walk. Compared to

stance time calculated in (Selles et al., 2005) (i.e.,

15 ms ± 41 ms), in (Rampp et al., 2015) (i.e.,

9 ms ± 69 ms), and in (Salarian et al., 2004) (i.e.,

5.9 ms ± 29.6 ms), the accuracy is similar but the

precision is improved in our method (i.e.,

−6 ms ±15 ms). Similar accuracy and better

precision in stride time are also found in our method

0.55 0.6 0.65 0.7 0.75 0.8

−80

−60

−40

−20

(Stance time

accel

+Stance time

ref

)/2[s]

Stance time

accel

−Stance time

ref

[ms]

0.32 0.34 0.36 0.38 0.4 0.42 0.44 0.46 0.48 0.5 0.52

−80

−60

−40

−20

(Swing time

accel

+Swing time

ref

)/2[s]

Swing time

accel

−Swing time

ref

[ms]

0.9 0.95 1 1.05 1.1 1.15 1.2 1.25 1.3

−80

−60

−40

−20

(Stride time

accel

+Stride time

ref

)/2[s]

Stride time

accel

−Stride time

ref

[ms]

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing

(i.e., −6 ms ± 17 ms) compared to (Rampp et al.,

2015) (i.e., 2 ms ± 68 ms) and to (Salarian et al.,

2004) (i.e., 2.2 ms ± 23.2 ms). In addition, the

accuracy of swing time in (Rampp et al., 2015) (i.e.,

−8 ms ± 45 ms) is similar to our results but the

precision is improved in our method (i.e.,

−5 ms ±17 ms). Compared to commercial trunk

accelerometer systems (e.g., (Auvinet et al., 1999)),

which only provide global gait features, our system

is capable to extract stride-by-stride parameters. The

stride-by-stride extraction may be a huge advantage

in the gait analysis of some specific population such

as Parkinson’s disease patients who experience

freezing of gait, a sudden and brief episodic

alteration of strides regulation.

Participants did not complain about the hardware

system during the gait tests. They all reported that

wires and accelerometers did not interfere with their

gait. Since only two accelerometers were attached to

heels and wires were behind the legs of the

participants during walking, these participants did

not notice or complain about the system.

It is noteworthy that all accelerometers of the

used hardware system were synchronized. The

algorithm can thus extract other important gait

parameters such as the times of initial double

support, terminal double support, double support,

and right/left steps.

Based on TS and HO, the algorithm can extract

the durations of the sub-phases of the stance phase,

namely: (1) loading response duration (time from

HS of one foot to TS of the same foot); (2) foot-flat

duration (time from TS of one foot to HO of the

same foot); and (3) push-off duration (time from HO

of one foot to TO of the same foot). In addition, HC

can be used to refine the swing phase duration.

The proposed ambulatory accelerometer system

was capable of measuring temporal gait parameters

in a very large number of strides without the need of

controlled laboratory conditions. We believe that our

novel accelerometer-based system offers

perspectives for use in a routine clinical practice to

deal with abnormal gait (e.g., gait of patients with

Parkinson’s disease).

5 CONCLUSIONS

We presented a new signal processing algorithm that

reduces the number of wearable accelerometers for

estimating temporal gait parameters. The advantages

of this method can be summarized as follows:

• Only two accelerometers are required, i.e., one for

each shoe at the level of the heel. This contributes

to a simplification of our wearable accelerometer-

based system, thus resulting in reducing the costs

and time needed to attach the system on body.

• This algorithm is validated for consecutive strides

during normal walking. The validation used

reference data provided by a kinematic system

(used as gold standard) and a video camera.

• Compared to previous studies, the proposed

method performs equally well or better in terms of

accuracy and precision of detection of temporal

gait parameters such as times of swing, stance, and

stride phases.

The extension of this method to the study of

pathological gait (e.g., gait of patients with

Parkinson’s disease) is now in progress. The method

promises to allow an objective quantification of gait

parameters in routine clinical practice.

ACKNOWLEDGEMENTS

The authors wish to acknowledge the contribution of

J. Stamatakis and B. Macq through the design of the

accelerometer-based hardware system used in the

present study. The authors would like also to thank

all the participants to the gait tests of this study.

REFERENCES

Aminian, K., Rezakhanlou, K., De Andres, E., Fritsch, C.,

Leyvraz, P.-F., and Robert, P. (1999). Temporal

feature estimation during walking using miniature

accelerometers: an analysis of gait improvement after

hip arthroplasty. Medical and Biological Engineering

and computing, 37(6):686–691.

Auvinet, B., Chaleil, D., and Barrey, E. (1999). Analyse

de la marche humaine dans la pratique hospitalière par

une méthode accélérométrique. Revue du Rhumatisme,

66(7–9) :447–457.

Boutaayamou, M., Schwartz, C., Denoël, V., Forthomme,

B., Croisier, J.-L., Garraux, G., Verly, J., and Brüls,

O. (2014). Development and validation of a 3D

kinematic-based method for determining gait events

during overground walking. In IEEE International

Conference on 3D Imaging, pages 1–6.

Boutaayamou, M., Schwartz, C., Stamatakis, J., Denoël,

V., Maquet, D., Forthomme, B., Croisier, J.-L., Macq,

B., Verly, J., Garraux, G., and Brüls, O. (2015a).

Development and validation of an accelerometer-

based method for quantifying gait events. Medical

Engineering and Physics, 37:226–232.

Boutaayamou, M., Denoël, V., Verly, J., Garraux, G., and

Brüls, O. (2015b). Gait segmentation using continuous

wavelet transform for extracting validated gait events

from accelerometer signals. In Biomedica 2015–The

Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers

European Life Sciences Summit.

Godfrey, A., Conway, R., Meagher, D., and ÓLaighin, G.

(2008). Direct measurement of human movement by

accelerometry. Medical Engineering and Physics,

30(10):1364–1386.

Forsman, P. M., Toppila, E. M., and Hæggström, E. O.

(2009). Wavelet analysis to detect gait events. In IEEE

EMBC, pages 424–427.

Khandelwal, S., and Wickström, N. (2014). Identification

of gait events using expert knowledge and continuous

wavelet transform analysis. In Proceedings of the

International Conference on Bio-inspired Systems and

Signal Processing, pages 197–204.

Lee, J.-.A., Cho S.-H., Lee J.-W., Lee K.-H., and Yang H.-

K. (2007). Wearable accelerometer system for

measuring the temporal parameters of gait. In

Proceedings of the 29th Annual International

Conference of the IEEE EMBS, pages 23-26.

Rampp, A., Barth, J., Schülein, S., Gaßmann, K.-G.,

Klucken, J., and Eskofier, B. M. (2015). Inertial

sensor-based stride parameter calculation from gait

sequences in geriatric patients. IEEE Transactions on

Biomedical Engineering, 62(4):1089–1097.

Rueterbories, J., Spaich., E.G., Larsen, B., and Andersen

O.K. (2010). Methods for gait event detection and

analysis in ambulatory systems. Medical Engineering

and Physics, 32(6):545–552.

Salarian, A., Russmann, H., Vingerhoets, F.J.G.,

Dehollain, C., Blanc, Y., Burkhard P.R., and Aminian,

K. (2004). Gait assessment in Parkinson’s disease:

toward an ambulatory system for long-term

monitoring. IEEE Transactions on Biomedical

Engineering, 51(8):1434–1443.

Selles, R.W., Formanoy, M.A.G., Bussmann, J.B.J.,

Janssens, P.J., and Stam, H.J. (2005). Automated

estimation of initial and terminal contact timing using

accelerometers; development and validation in

transtibial amputees and controls. IEEE Transactions

on Neural Systems and Rehabilitation Engineering,

13(1):81–88.

Stamatakis, J., Crémers, J., Maquet, D., Macq, B., and

Garraux, G. (2011). Gait feature extraction in

Parkinson’s disease using low-cost accelerometers. In

Proceedings of the Annual International Conference

of the IEEE Engineering in Medicine and Biology

Society, pages 7900–7903.

Whittle, W. (1996). Clinical gait analysis: a review.

Human Movement Science, 15:369–387.

Willemsen, A.T.M., Bloemhof, F., and Boom, H.B.

(1990). Automatic stance-swing, phase detection from

accelerometer data for peroneal nerve stimulation.

IEEE Transactions on Biomedical Engineering,

37(12):1201–8.

APPENDIX

We present the piecewise-linear fitting method used

to estimate the locations of the convex curvature in a

signal (Sec. 2.4.1). For this, we consider a given

signal =

(





)

,(



),…, (



) defined

in a time interval=



,



,…,



, where  is the

total number of samples of . This method first

computes the coefficients of piecewise-linear

functions with two linear segments that best fit 

in the least-square sense, leading to the computation

of least-square errors. The minimum of these least-

square errors is then determined and the associated

piecewise-linear function provides two linear

segments that intersect at the breakpoint

(





,(



)

. The main steps to determine the

breakpoint

(





,(



)

are as follows:

• For each =1,…,, one computes the

coefficients 



,



,



,and



of a piecewise-linear

function 



that best fits  by minimizing





=((



)





−



(



))²,

(2)

where





()=





∗+



,∈







,







∗+



,∈

]





,



]



(3)

This error can be expressed as





‖







−

‖





(4)

where





=









,



=







⋮





⋮

,

=

(



)

⋮

(



)

i

∈







,



and





=









,



=







⋮





⋮

,

=

(



)

⋮

(



)

i

∈

]





,



]

.

The normal equations associated with (4) are















.



(5)

Solving (5) leads to the coefficients





,



,



,and



• Finally, one obtains the breakpoint

(





,(



)

by determining the minimum of the

least-square errors, i.e.,





=

,…,

(





)



(6)

BIOSIGNALS 2016 - 9th International Conference on Bio-inspired Systems and Signal Processing