Detection of muscle fatigue by the surface electromyogram and its application

金沢大学理工研究域電子情報学系金沢大学理工研究域電子情報学系The muscle is moved by muscle fiber contraction receiving command from the brain. But, energy that moves muscle is not infinity. If muscle get into energy shortage, no matter how send command from the brain, muscle is not moved. Such a temporary muscular dysfunction is muscle fatigue. If muscle becomes excess fatigue condition, it may decrease work efficiency, or muscle strain. If we are able to measure muscle fatigue objectively, improve work efficiency, or avert muscle strain. Therefore, it is necessity to measure muscle fatigue. It is able to objectively measure with a surface electromyogram(EMG). The feature of muscle fatigue are increase in amplitude and make the transition from high frequency spectrum to low frequency spectrum. We evaluate muscle fatigue Mean Power Frequency (MPF). to evaluates frequency of surface EMG.We assume muscle recovery process is converse phenomenon from muscle fatigue, and it is able to evaluate elevated MPF. The purpose of the present study is to design of system that effective training, or improve work efficiency, or avert muscle strain uses feature of muscular fatigue and muscle recovery process. © 2010 IEEE

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

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

Electromyography - A Reliable Technique for Muscle Activity Assessment

In recent years, many questions have been raised on the credibility of Electromyography (EMG) as a technique to evaluate muscle activity, particularly by sports and fitness community. This questioning goes farther when it comes to surface electromyography (sEMG). This paper covers an overview of EMG, addresses some basic concepts and provide rudiment for research. Muscle activity assessment through EMG has been reviewed in terms of the type of movements. There are few limitations to EMG but these confines are addressable. The problem rather lies in the interpretation and generalization of that data. Limitations are there in every technology, precautionary measures must be taken to avoid those while using it. Reservations about EMG have been summarized along with their responses. A few techniques to analyze EMG data, and possibilities to extrapolate and interpret, are also provided. Current perspectives and practical applications of EMG and sEMG are also part of this article

Surface mechanomyography and electromyography provide non-invasive indices of inspiratory muscle force and activation in healthy subjects

The current gold standard assessment of human inspiratory muscle function involves using invasive measures of transdiaphragmatic pressure (Pdi) or crural diaphragm electromyography (oesEMGdi). Mechanomyography is a non-invasive measure of muscle vibration associated with muscle contraction. Surface electromyogram and mechanomyogram, recorded transcutaneously using sensors placed over the lower intercostal spaces (sEMGlic and sMMGlic respectively), have been proposed to provide non-invasive indices of inspiratory muscle activation, but have not been directly compared to gold standard Pdi and oesEMGdi measures during voluntary respiratory manoeuvres. To validate the non-invasive techniques, the relationships between Pdi and sMMGlic, and between oesEMGdi and sEMGlic were measured simultaneously in 12 healthy subjects during an incremental inspiratory threshold loading protocol. Myographic signals were analysed using fixed sample entropy (fSampEn), which is less influenced by cardiac artefacts than conventional root mean square. Strong correlations were observed between: mean Pdi and mean fSampEn |sMMGlic| (left, 0.76; right, 0.81), the time-integrals of the Pdi and fSampEn |sMMGlic| (left, 0.78; right, 0.83), and mean fSampEn oesEMGdi and mean fSampEn sEMGlic (left, 0.84; right, 0.83). These findings suggest that sMMGlic and sEMGlic could provide useful non-invasive alternatives to Pdi and oesEMGdi for the assessment of inspiratory muscle function in health and disease.Peer ReviewedPostprint (published version