A Data-Driven Approach to Predict Fatigue in Exercise Based on Motion Data from Wearable Sensors or Force Plate.

Jiang, Yanran; Hernandez, Vincent; Venture, Gentiane; Kulic, Dana; K Chen, Bernard

Jiang, Yanran; Hernandez, Vincent; Venture, Gentiane; Kulic, Dana; K Chen, Bernard.

Afiliación

Jiang Y; Mechanical and Aerospace Department, Monash University, Melbourne, VIC 3800, Australia.
Hernandez V; Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-0012, Japan.
Venture G; Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-0012, Japan.
Kulic D; Mechanical and Aerospace Department, Monash University, Melbourne, VIC 3800, Australia.
K Chen B; Mechanical and Aerospace Department, Monash University, Melbourne, VIC 3800, Australia.

Sensors (Basel) ; 21(4)2021 Feb 22.

Article en En | MEDLINE | ID: mdl-33671497

Asunto(s)

Ejercicio Físico; Dispositivos Electrónicos Vestibles; Fenómenos Biomecánicos; Fatiga/diagnóstico; Femenino; Humanos; Masculino; Movimiento (Física)

Palabras clave

IMU; deep learning; fatigue estimation; force plate; human motion data; machine learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Ejercicio Físico / Dispositivos Electrónicos Vestibles Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Límite: Female / Humans / Male Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Australia

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