Muscle fatigue analysis in isometric contractions using geometric features of surface electromyography signals. (July 2021)

• Record Type: Journal Article
• Authors: S., Edward Jero; K., Divya Bharathi; P.A., Karthick; S., Ramakrishnan
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Surface electromyography -- Muscle fatigue -- Discrete Fourier transform -- Boundary detection -- Fourier descriptors -- Shape analysis -- Geometric features; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102603
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Muscle fatigue detection using sEMG signals plays a vital role in preventing muscle injuries. Frequency domain based geometric features are proposed to analyse muscle fatiguing contractions. Geometric features are extracted from the shape formed in the complex plane representation of Discrete Fourier transform. k-nearest neighbor, naïve Bayes, decision tree and multilayer perceptron (MLP) classifiers are employed to differentiate muscle nonfatigue and fatigue conditions. A maximum accuracy of 86 % is achieved with five selected features and MLP based detection model. Abstract: In this study, an attempt has been made to differentiate the muscle nonfatigue and fatigue conditions using geometric features of surface Electromyography (sEMG) signals. For this purpose, a new framework is proposed that consists of Fourier descriptor based shape representation and geometric feature extraction. The sEMG signals are acquired from biceps brachii muscle of 25 healthy adult volunteers in isometric contractions. The signals associated with nonfatigue and fatigue conditions are preprocessed and subjected to discrete Fourier transform. The Fourier coefficients are scattered in the complex plane and the envelope is computed using α -shape method. The boundary of the resultant shape represents the Fourier descriptors. The geometric features namely centroid, moments, perimeter, area, circularity, convexity, average bending energy, major axis length, eccentricity and ellipse variance are extracted from the shape. The results show that seven out of twelve features have statistically significant (p < 0.001) difference between the two conditions. The five features namely major axis length, area, perimeter, second order moment and central moment are considered for muscle fatigue classification using k-nearest neighbor, naïve Bayes, decision tree and multilayer perceptron (MLP). Among these classifiers, maximum accuracy of 86 % is achieved with MLP based detection model. Therefore, it appears that the geometric features of sEMG signals could be useful in the detection of muscle fatigue condition in clinical diagnosis, workplace and rehabilitation.