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Review
. 2011;11(4):3545-94.
doi: 10.3390/s110403545. Epub 2011 Mar 24.

A review of non-invasive techniques to detect and predict localised muscle fatigue

Mohamed R Al-Mulla  1 , Francisco Sepulveda, Martin Colley
Affiliations
Review

A review of non-invasive techniques to detect and predict localised muscle fatigue

Mohamed R Al-Mulla et al. Sensors (Basel). 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: classification; feature extraction; muscle fatigue; sEMG.

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Figures

Figure 1.
Figure 1.
Experimental setup for an autonomous system to detect or predict fatigue.
Figure 2.
Figure 2.
Movement aspects from one of the trials which aided in labelling the sEMG signal.
Figure 3.
Figure 3.
The use of elbow angle to label and classify the signal.
Figure 4.
Figure 4.
The use of angular oscillation to label and classify the signal.
See this image and copyright information in PMC

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MeSH terms