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A Review of Non-invasive Techniques to Detect and Predict Localised Muscle Fatigue

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2011 Dec 14
PMID 22163810
Citations 67
Authors
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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.

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References
1.
Hagberg M . Work load and fatigue in repetitive arm elevations. Ergonomics. 1981; 24(7):543-55. DOI: 10.1080/00140138108924875. View

2.
Bonato P, Roy S, Knaflitz M, De Luca C . Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans Biomed Eng. 2001; 48(7):745-53. DOI: 10.1109/10.930899. View

3.
Gandevia S, Enoka R, McComas A, Stuart D, Thomas C . Neurobiology of muscle fatigue. Advances and issues. Adv Exp Med Biol. 1995; 384:515-25. DOI: 10.1007/978-1-4899-1016-5_39. View

4.
Bezdek J, Pal N . Some new indexes of cluster validity. IEEE Trans Syst Man Cybern B Cybern. 2008; 28(3):301-15. DOI: 10.1109/3477.678624. View

5.
Petrofsky J . Quantification through the surface EMG of muscle fatigue and recovery during successive isometric contractions. Aviat Space Environ Med. 1981; 52(9):545-50. View