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

被引:196
作者
Al-Mulla, Mohamed R. [1 ]
Sepulveda, Francisco [1 ]
Colley, Martin [1 ]
机构
[1] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England
关键词
muscle fatigue; sEMG; feature extraction; classification; SURFACE ELECTROMYOGRAM RECORDINGS; TIME-FREQUENCY ANALYSIS; MEAN POWER FREQUENCY; EMG SIGNAL ANALYSIS; BICEPS-BRACHII; DYNAMIC CONTRACTIONS; MYOELECTRIC SIGNALS; ACOUSTIC MYOGRAPHY; MECHANOMYOGRAPHIC RESPONSES; VOLUNTARY CONTRACTION;
D O I
10.3390/s110403545
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页码:3545 / 3594
页数:50
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