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A Hybrid Solution for Human Activity Recognition Application to Wrist-Worn Accelerometry

M. Abbas 1 M. Saleh 1 R. Le Bouquin Jeannès 1
1 LTSI - Laboratoire Traitement du Signal et de l'Image
Abstract : This paper presents a systematic approach that is able to classify activities of daily living using a wrist-worn triaxial accelerometer. The strategy, which divides the multi-class problem into several binary classification sub-problems, is a mixture of both feature thresholding method and machine learning. The idea behind our design is to separate cyclic activities from transient ones, then to provide explicit recognition in these two categories separately. Experimental results have successfully proven the effectiveness of the proposed activity recognition process. © 2019 IEEE.
Keywords : activity recognition classification feature thresholding machine learning wrist-worn accelerometer
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Conference papers
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-02472490
Contributor : Laurent Jonchère <>
Submitted on : Monday, February 10, 2020 - 11:43:16 AM
Last modification on : Tuesday, February 11, 2020 - 1:30:39 AM

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UNIV-RENNES1 | LTSI | UNIV-RENNES

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M. Abbas, M. Saleh, R. Le Bouquin Jeannès. A Hybrid Solution for Human Activity Recognition Application to Wrist-Worn Accelerometry. 5th International Conference on Advances in Biomedical Engineering, ICABME 2019, Oct 2019, Tripoli, Lebanon. pp.8940233, ⟨10.1109/ICABME47164.2019.8940233⟩. ⟨hal-02472490⟩

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