Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals

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 contrac...

Full description

Saved in:
Bibliographic Details
Published in:Computer methods in biomechanics and biomedical engineering 2022-02, Vol.25 (3), p.320-332
Main Authors: K, Divya Bharathi, P. A., Karthick, S., Ramakrishnan
Format: Article
Language:English
Subjects:
Algorithms
Alpha shapes
Artificial neural networks
Automation
Biomarkers
cyclostationarity
Density
Electromyography
Electromyography - methods
Fast Fourier transformations
Fatigue
Fatigue analysis
Feature extraction
Fourier Analysis
Fourier transforms
geometric features
Humans
Learning algorithms
Machine learning
Mathematical models
Multilayer perceptrons
Muscle Contraction
Muscle fatigue
Muscle Fatigue - physiology
Muscle, Skeletal - physiology
Muscles
Muscular fatigue
Signal classification
Spectral correlation
surface electromyography
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1025-5842
1476-8259
DOI:10.1080/10255842.2021.1955104