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Sensors 2014, 14(2), 2052-2070; doi:10.3390/s140202052

Towards Whole Body Fatigue Assessment of Human Movement: A Fatigue-Tracking System Based on Combined sEMG and Accelerometer Signals

1
School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward, K1N 6N5, Ottawa, ON, Canada
2
Division of Engineering, New York University Abu Dhabi, P.O. Box 129188, Abu Dhabi, UAE
3
Learning Algorithms and Systems Laboratory, Ecole Polytechnique Federale de Lausanne (EPFL), Station 9, CH 1015, Lausanne, Switzerland
*
Author to whom correspondence should be addressed.
Received: 2 December 2013 / Revised: 16 January 2014 / Accepted: 17 January 2014 / Published: 27 January 2014
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
View Full-Text   |   Download PDF [1575 KB, 21 June 2014; original version 21 June 2014]   |  

Abstract

This paper proposes a method to assess the overall fatigue of human body movement. First of all, according to previous research regarding localized muscular fatigue, a linear relation is assumed between the mean frequency and the muscular working time when the muscle is experiencing fatigue. This assumption is verified with a rigorous statistical analysis. Based on this proven linearity, localized muscular fatigue is simplified as a linear model. Furthermore, localized muscular fatigue is considered a dynamic process and, hence, the localized fatigue levels are tracked by updating the parameters with the most current surface electromyogram (sEMG) measurements. Finally, an overall fatigue level is computed by fusing localized muscular fatigue levels. The developed fatigue-tracking system is evaluated with two fatigue experiments (in which 10 male subjects and seven female subjects participated), including holding self-weight (dip start position training) and lifting weight with one arm (arm curl training). View Full-Text
Keywords: localized muscular fatigue; fatigue information fusion; wireless wearable system localized muscular fatigue; fatigue information fusion; wireless wearable system
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Dong, H.; Ugalde, I.; Figueroa, N.; El Saddik, A. Towards Whole Body Fatigue Assessment of Human Movement: A Fatigue-Tracking System Based on Combined sEMG and Accelerometer Signals. Sensors 2014, 14, 2052-2070.

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