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A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue
School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
* Author to whom correspondence should be addressed.
Received: 7 January 2011; in revised form: 1 March 2011 / Accepted: 21 March 2011 / Published: 24 March 2011
Abstract: Muscle fatigue is an established area of research and various types of muscle fatigue have been investigated in order to fully understand the condition. This paper gives an overview of the various non-invasive techniques available for use in automated fatigue detection, such as mechanomyography, electromyography, near-infrared spectroscopy and ultrasound for both isometric and non-isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who wish to select the most appropriate methodology for research on muscle fatigue detection or prediction, or for the development of devices that can be used in, e.g., sports scenarios to improve performance or prevent injury. To date, research on localised muscle fatigue focuses mainly on the clinical side. There is very little research carried out on the implementation of detecting/predicting fatigue using an autonomous system, although recent research on automating the process of localised muscle fatigue detection/prediction shows promising results.
Keywords: muscle fatigue; sEMG; feature extraction; classification
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Cite This Article
MDPI and ACS Style
Al-Mulla, M.R.; Sepulveda, F.; Colley, M. A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors 2011, 11, 3545-3594.
Al-Mulla MR, Sepulveda F, Colley M. A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors. 2011; 11(4):3545-3594.
Al-Mulla, Mohamed R.; Sepulveda, Francisco; Colley, Martin. 2011. "A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue." Sensors 11, no. 4: 3545-3594.