A Machine Learning Pipeline for Gait Analysis in a Semi Free-Living Environment

This paper presents a novel approach to creating a graphical summary of a subject’s activity during a protocol in a Semi Free-Living Environment. Thanks to this new visualization, human behavior, in particular locomotion, can now be condensed into an easy-to-read and user-friendly output. As time se...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 23; H. 8; S. 4000
Hauptverfasser:	Jung, Sylvain, de l’Escalopier, Nicolas, Oudre, Laurent, Truong, Charles, Dorveaux, Eric, Gorintin, Louis, Ricard, Damien
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 14.04.2023 MDPI
Schlagworte:	Algorithms change point detection Data mining Falls Feedback free-living Gait Gait Analysis graphical feedback Health care Human Activity Recognition Human acts Human behavior Humans IMU Life Sciences Locomotion Longitudinal studies Machine Learning Pathology Pipe lines Signal processing Visualization (Computers) Wearable Electronic Devices wearable sensor France IMU change point detection wearable sensor Human Activity Recognition free-living graphical feedback C Ricard E Jung L L. Truong S. de l'Escalopier E. Gorintin D. A Machine free-living wearable sensor IMU graphical feedback change point detection Human Activity Recognition N S L. Ricard C. Dorveaux de l'Escalopier Oudre Gorintin Truong D. A Machine free-living N. Oudre Dorveaux
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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