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Content Provider | IET Digital Library |
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Author | Krishnamani, Divya Bharathi P. A., Karthick Swaminathan, Ramakrishnan |
Abstract | Surface electromyography (sEMG) signals are stochastic, multicomponent and non-stationary, and therefore their interpretation is challenging. In this study, an attempt has been made to develop an automated muscle fatigue detection system using variational mode decomposition (VMD) features of sEMG signals and random forest classifier. The sEMG signals are acquired from 103 healthy volunteers during isometric (45 subjects) and dynamic (58 subjects) muscle fatiguing contractions and preprocessed. The band-limited intrinsic mode functions (BLIMFs) are extracted from non-fatigue and fatigue segments of the signals using the VMD algorithm. Hjorth features, such as activity, mobility and complexity are extracted from each BLIMF and are given to the random forest classifier. The performance of these features is evaluated using leave-one-subject-out cross-validation. The results show that the complexity feature performs better than others and it has resulted in an accuracy of 83% in dynamic contractions and 80% in isometric contractions. The performance is increased by about 8% in a dynamic condition when the most significant complexity features (p < 0.001) are used and by about 12% for isometric when the authors use all significant features. Therefore, the proposed approach could be used to detect fatigue conditions in various neuromuscular activities and real-time monitoring in the workplace. |
Starting Page | 745 |
Ending Page | 753 |
Page Count | 9 |
ISSN | 17519675 |
Volume Number | 14 |
e-ISSN | 17519683 |
Issue Number | Issue 10, Dec (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2020.0315 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-spr/14/10 |
Journal | IET Signal Processing |
Publisher Date | 2021-01-05 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | 103 Healthy Volunteers 45 Subjects 58 Subjects Automated Muscle Fatigue Detection System Band-limited Intrinsic Mode Function Bioelectric Signal Biology And Medical Computing Biomechanics BLIMF Complexity Feature Computer Vision And Image Processing Technique Digital Signal Processing Dynamic Condition Dynamic Contractions Dynamic Muscle Fatiguing Contractions Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electromyography Fatigue Fatigue Condition Fatigue Segments Feature Extraction Hjorth Feature Isometric Contractions Leave-one-subject-out Cross-validation Medical Signal Processing Mobility Muscle Probability Theory Random Forest Classifier SEMG Signal Signal Processing And Detection Significant Complexity Feature Statistics Stochastic Linearised SCUC Surface Electromyography Signal Variational Mode Decomposition Based Differentiation Variational Mode Decomposition Feature VMD Algorithm |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering |
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