Article

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

Authors:

Gaëtan GarrauxAuthors Info & Claims

BIOSTEC 2016: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies

Pages 57 - 66

https://doi.org/10.5220/0005696900570066

Published: 21 February 2016 Publication History

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 subjectâ 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: â 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 â 6 ms ± 15 ms, â 5 ms ± 17 ms, and â 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 Parkinsonâ s disease).

Index Terms

Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers
1. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems

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Information

Published In

BIOSTEC 2016: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies

February 2016

1642 pages

ISBN:9789897581700

Editors:
James Gilbert
University of Hull
,
Haim Azhari
Technion-Israel Institute of Technology
,
Hesham Ali
University of Nebraska at Omaha
,
Carla Quintão
FCT-UNL
,
Jan Sliwa
Bern University of Applied Sciences
,
Carolina Ruiz
WPI
,
Ana Fred
Instituto de Telecomunicações / IST
,
Hugo Gamboa
LIBPHYS-UNL / FCT - New University of Lisbon

Publisher

SCITEPRESS - Science and Technology Publications, Lda

Setubal, Portugal

Publication History

Published: 21 February 2016

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Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease

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