Authors:
Mohamed Boutaayamou
1
;
Vincent Denoël
2
;
Olivier Brüls
2
;
Marie Demonceau
3
;
Didier Maquet
3
;
Bénédicte Forthomme
2
;
Jean-Louis Croisier
2
;
Cédric Schwartz
2
;
Jacques G. Verly
3
and
Gaëtan Garraux
4
Affiliations:
1
University of Liège (ULg) and ULg, Belgium
;
2
University of Liège (ULg), Belgium
;
3
ULg, Belgium
;
4
ULg and University Hospital Center, Belgium
Keyword(s):
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.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Detection and Identification
;
Devices
;
Health Information Systems
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Physiological Computing Systems
;
Wearable Sensors and Systems
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).
(More)