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

Mohamed Boutaayamou, Vincent Denoël, Olivier Brüls, Marie Demonceau, Didier Maquet, Bénédicte Forthomme, Jean-Louis Croisier, Cédric Schwartz, Jacques G. Verly, Gaëtan Garraux

2016

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).

Paper Citation

in Harvard Style

Boutaayamou M., Denoël V., Brüls O., Demonceau M., Maquet D., Forthomme B., Croisier J., Schwartz C., Verly J. and Garraux G. (2016). Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 57-66. DOI: 10.5220/0005696900570066

in Bibtex Style

@conference{biosignals16,
author={Mohamed Boutaayamou and Vincent Denoël and Olivier Brüls and Marie Demonceau and Didier Maquet and Bénédicte Forthomme and Jean-Louis Croisier and Cédric Schwartz and Jacques G. Verly and Gaëtan Garraux},
title={Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},
year={2016},
pages={57-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005696900570066},
isbn={978-989-758-170-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers
SN - 978-989-758-170-0
AU - Boutaayamou M.
AU - Denoël V.
AU - Brüls O.
AU - Demonceau M.
AU - Maquet D.
AU - Forthomme B.
AU - Croisier J.
AU - Schwartz C.
AU - Verly J.
AU - Garraux G.
PY - 2016
SP - 57
EP - 66
DO - 10.5220/0005696900570066