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Sensors 2011, 11(2), 1542-1557;

An Autonomous Wearable System for Predicting and Detecting Localised Muscle Fatigue

School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
Author to whom correspondence should be addressed.
Received: 15 December 2010 / Revised: 12 January 2011 / Accepted: 19 January 2011 / Published: 27 January 2011
(This article belongs to the Section Physical Sensors)
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Muscle fatigue is an established area of research and various types of muscle fatigue have been clinically investigated in order to fully understand the condition. This paper demonstrates a non-invasive technique used to automate the fatigue detection and prediction process. The system utilises the clinical aspects such as kinematics and surface electromyography (sEMG) of an athlete during isometric contractions. Various signal analysis methods are used illustrating their applicability in real-time settings. This demonstrated system can be used in sports scenarios to promote muscle growth/performance or prevent injury. To date, research on localised muscle fatigue focuses on the clinical side and lacks the implementation for detecting/predicting localised muscle fatigue using an autonomous system. Results show that automating the process of localised muscle fatigue detection/prediction is promising. The autonomous fatigue system was tested on five individuals showing 90.37% accuracy on average of correct classification and an error of 4.35% in predicting the time to when fatigue will onset. View Full-Text
Keywords: muscle fatigue; sEMG; feature extraction; classification muscle fatigue; sEMG; feature extraction; classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Al-Mulla, M.R.; Sepulveda, F.; Colley, M. An Autonomous Wearable System for Predicting and Detecting Localised Muscle Fatigue. Sensors 2011, 11, 1542-1557.

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