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.
Highlights
Muscle fatigue is a long lasting reduction of the ability to contract and exert force
The term EMG appeared after the first recording of electrical muscle activity during voluntary contractions was made by Marey in 1890, who based this work on the discovery by Dubois-Raymonds in 1849 that such a recording would be possible
De Luca is probably the most influential worker on EMG, he warned that it is important to understand the short-comings of EMG, and in his paper on “The Use of surface electromyography (sEMG) in Biomechanics”, he argued that sEMG is such an easy tool to use in muscle physiology that it can be abused
Summary
Muscle fatigue is a long lasting (several hours) reduction of the ability to contract and exert force. An automated system will guide the user in his training by acting as a warning device before fatigue sets in while maintaining an optimal fatigue state, promoting improvements and avoiding unnecessary strain on the muscle so as to prevent injury This system can be applied in occupational health and ergonomics, in particular where there is a risk of work-related musculoskeletal disorders. In fatiguing contractions in young and old adults at different MVC, older subjects experience increased electromyographic activity levels [11] This suggests that findings are influenced significantly by age and gender differences, such that the results from a twenty year old man and a sixty year old woman may not be comparable. A typical experiment in muscle fatigue research involves a subject performing a set task such as moving a given limb in a specified manner while sensors attached to the skin detect changes in signals arising from the movement. This paper will review the current state of the art in sEMG signal analysis techniques, such as the different methods of signal acquisition, whose features may be used for signal analysis, and signal classification, so that the reader may identify the most appropriate methods that may be applied during clinical diagnosis, sports injury prevention, biomedical research, hardware implementation and end user applications
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