Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals. Issue 3 (17th February 2022)

• Record Type: Journal Article
• Authors: K, Divya Bharathi; P. A., Karthick; S., Ramakrishnan
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 25:Issue 3(2022)
• Page Start: 320
• Page End: 332
• Publication Date: 2022-02-17
• Subjects: Fatigue analysis -- surface electromyography -- cyclostationarity -- geometric features -- artificial neural networks; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2021.1955104
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: In this study, an attempt has been made to develop an automated muscle fatigue detection system using cyclostationary based geometric features of surface electromyography (sEMG) signals. For this purpose, signals are acquired from fifty-eight healthy volunteers under dynamic muscle fatiguing contractions. The sEMG signals are preprocessed and the epochs of signals under nonfatigue and fatigue conditions are considered for the analysis. A computationally effective Fast Fourier transform based accumulation algorithm is adapted to compute the spectral correlation density coefficients. The boundary of spectral density coefficients in the complex plane is obtained using alpha shape method. The geometric features, namely, perimeter, area, circularity, bending energy, eccentricity and inertia are extracted from the shape and the machine learning models based on multilayer perceptron (MLP) and extreme learning machine (ELM) are developed using these biomarkers. The results show that the cyclostationarity increases in fatigue condition. All the extracted features are found to have significant difference in the two conditions. It is found that the ELM model based on prominent features classifies the sEMG signals with a maximum accuracy of 94.09% and F-score of 93.75%. Therefore, the proposed approach appears to be useful for analysing the fatiguing contractions in neuromuscular conditions.