Al-Mulla, Mohamed R and Sepulveda, Francisco and Colley, Martin (2011) 'An autonomous wearable system for predicting and detecting localised muscle fatigue.' Sensors, 11 (2). pp. 1542-1557. ISSN 1424-8220

Preview

Text sensors-11-01542.pdf Available under License Creative Commons Attribution. Download (2MB) | Preview

Abstract

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. © 2011 by the authors; licensee MDPI, Basel, Switzerland.

Item Type:	Article
Uncontrolled Keywords:	muscle fatigue; sEMG; feature extraction; classification
Subjects:	R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	14 Aug 2012 10:17
Last Modified:	23 Sep 2022 19:05
URI:	http://repository.essex.ac.uk/id/eprint/3463

Actions (login required)

View Item