Article: Detection of fatigue on gait using accelerometer data and supervised machine learning Journal: International Journal of Grid and Utility Computing (IJGUC) 2020 Vol.11 No.4 pp.474 - 485 Abstract: In this paper, we aim to detect the fatigue based on accelerometer data from human gait using traditional classifiers from machine learning. First, we compare widely used machine learning classifiers to know which classifier can detect fatigue with the fewest errors. We observe that the best results were obtained with a Support Vector Machine (SVM) classifier. Later, we propose a new approach to solve the feature selection problem to know which features are more relevant to detect fatigue in healthy people based on their gait patterns. Finally, we used relevant gait features discovered in a previous step as input in classifiers used previously to know its impact on the classification process. Our results indicate that using only some gait features selected by our proposed feature selection method it is possible to improve fatigue detection based on data from human gait. We conclude that it is possible to distinguish between a normal gait person and a fatigued gait person with high accuracy. Inderscience Publishers - linking academia, business and industry through research

Title: Detection of fatigue on gait using accelerometer data and supervised machine learning

Authors: Dante Arias-Torres; José Adán Hernández-Nolasco; Miguel A. Wister; Pablo Pancardo

Addresses: Academic Division of Informatics and Systems, Juarez Autonomous University of Tabasco, Villahermosa, Tabasco, Mexico ' Academic Division of Informatics and Systems, Juarez Autonomous University of Tabasco, Villahermosa, Tabasco, Mexico ' Academic Division of Informatics and Systems, Juarez Autonomous University of Tabasco, Villahermosa, Tabasco, Mexico ' Academic Division of Informatics and Systems, Juarez Autonomous University of Tabasco, Villahermosa, Tabasco, Mexico

Abstract: In this paper, we aim to detect the fatigue based on accelerometer data from human gait using traditional classifiers from machine learning. First, we compare widely used machine learning classifiers to know which classifier can detect fatigue with the fewest errors. We observe that the best results were obtained with a Support Vector Machine (SVM) classifier. Later, we propose a new approach to solve the feature selection problem to know which features are more relevant to detect fatigue in healthy people based on their gait patterns. Finally, we used relevant gait features discovered in a previous step as input in classifiers used previously to know its impact on the classification process. Our results indicate that using only some gait features selected by our proposed feature selection method it is possible to improve fatigue detection based on data from human gait. We conclude that it is possible to distinguish between a normal gait person and a fatigued gait person with high accuracy.

Keywords: gait; fatigue; detection; accelerometer; supervised learning.

DOI: 10.1504/IJGUC.2020.108475

International Journal of Grid and Utility Computing, 2020 Vol.11 No.4, pp.474 - 485

Received: 25 Feb 2019
Accepted: 29 Aug 2019
Published online: 14 Jul 2020 *

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Title: Detection of fatigue on gait using accelerometer data and supervised machine learning

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