Review of Application of Surface Electromyography Signals in Muscle Fatigue Research

doi:10.3778/j.issn.1673-9418.2312042

Abstract

Abstract: Muscle fatigue is a physiological phenomenon that occurs when muscles are overused or continuously loaded during exercise or labor. Currently, analyzing the fatigue mechanism is still a complex and multi-layered research problem. In recent years, research methods focusing on surface electromyographic (sEMG) signals have garnered significant attention. The application of advanced signal processing techniques and machine learning algorithms has enhanced the precision of interpreting surface electromyographic data, deepening our understanding of the mechanisms underlying muscle fatigue. This, in turn, provides crucial scientific support for improving athletic performance, preventing sports injuries, and enhancing rehabilitation treatments.This comprehensive review of muscle fatigue research based on surface electromyographic signals covers various aspects. First, the definition of muscle fatigue and currently commonly used detection methods are explained, and the characteristics and application scope of various methods are pointed out; Secondly, the EMG characteristics that characterize muscle fatigue are introduced in detail from linear characteristics such as time domain, frequency domain, time-frequency domain and the use of nonlinear parameters, and the advantages and limitations of these characteristics are also discussed; Thirdly, combining fatigue characteristics as input data, the classification algorithms commonly used for muscle fatigue are explored, and the applicable conditions, advantages and disadvantages of each algorithm are accurately summarized from the aspects of machine learning and deep learning algorithms; Finally, the challenges faced by muscle fatigue research at this stage are pointed out, and on the basis of proposing feasible solutions, the future research directions are prospected.

Key words: muscle fatigue, surface electromyographic, electromyographic features, machine learning, deep learning algorithms

摘要： Muscle fatigue is a physiological phenomenon that occurs when muscles are overused or continuously loaded during exercise or labor. Currently, analyzing the fatigue mechanism is still a complex and multi-layered research problem. In recent years, research methods focusing on surface electromyographic (sEMG) signals have garnered significant attention. The application of advanced signal processing techniques and machine learning algorithms has enhanced the precision of interpreting surface electromyographic data, deepening understanding of the mechanisms underlying muscle fatigue. This, in turn, provides crucial scientific support for improving athletic performance, preventing sports injuries, and enhancing rehabilitation treatments. This review of muscle fatigue research based on surface electromyographic signals covers various aspects. Firstly, the definition of muscle fatigue and current commonly used detection methods are explained, and the characteristics and application scope of various methods are pointed out. Secondly, the EMG characteristics that characterize muscle fatigue are introduced in detail from linear characteristics such as time domain, frequency domain, time-frequency domain and the use of nonlinear parameters, and the advantages and limitations of these characteristics are also discussed. Thirdly, combining fatigue characteristics as input data, the classification algorithms commonly used for muscle fatigue are explored, and the applicable conditions, advantages and disadvantages of each algorithm are accurately summarized from the aspects of machine learning and deep learning algorithms. Finally, the challenges faced by muscle fatigue research at this stage are pointed out, and on the basis of proposing feasible solutions, future research directions are prospected.

关键词: 肌肉疲劳, 表面肌电, 肌电特征, 机器学习, 深度学习算法

FANG Boru, QIU Dawei, BAI Yang, LIU Jing. Review of Application of Surface Electromyography Signals in Muscle Fatigue Research[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(9): 2261-2275.

方博儒, 仇大伟, 白洋, 刘静. 表面肌电信号在肌肉疲劳研究中的应用综述[J]. 计算机科学与探索, 2024, 18(9): 2261-2275.

References

[1] ST CLAIR GIBSON A, SWART J, TUCKER R. The interaction of psychological and physiological homeostatic drives and role of general control principles in the regulation of physiological systems, exercise and the fatigue process—the integrative governor theory[J]. European Journal of Sport Science, 2018, 18(1): 25-36.
[2] HUANG Z, CUI J, WANG Y, et al. Improving wheelchair user sitting posture to alleviate lumbar fatigue: a study utilizing sEMG and pressure sensors[J]. Frontiers in Neuro-science, 2024, 18: 1380150.
[3] 刘万阳, 李晓欧. 基于虚拟现实的肌电-计算机接口技术[J]. 生物医学工程研究, 2018, 37(2): 238-242.
LIU W Y, LI X O. Electromyography-computer interface?Technology based on virtual reality[J]. Journal of Biomedical Engineering Research, 2018, 37(2): 238-242.
[4] MARCO G, ALBERTO B, TAIAN V. Surface EMG and muscle fatigue: multi-channel approaches to the study of myoelectric manifestations of muscle fatigue[J]. Physiological Measurement, 2017, 38(5): 27-60.
[5] RANAVOLO A, SERRAO M, DRAICCHIO F. Critical issues and imminent challenges in the use of sEMG in return-to-work rehabilitation of patients affected by neurological disorders in the epoch of human-robot collaborative technologies[J]. Frontiers in Neurology, 2020, 11: 572069.
[6] LIU S H, LIN C B, CHEN Y, et al. An EMG patch for the real-time monitoring of muscle-fatigue conditions during exercise[J]. Sensors, 2019, 19(14): 3108.
[7] HEDAYATPOUR N, RASHID D M S, IZANLOO Z, et al. Men and women show different adaptations of quadriceps activity following fatiguing contractions: an explanation for the increased incidence of sports-related knee injuries in women?[J]. Journal of Electromyography and Kinesiology, 2021, 58: 102552.
[8] LIU X, LI Z. Influence mechanism of running sportswear fatigue based on BP neural network[J]. EURASIP Journal on Advances in Signal Processing, 2021(1): 1-15.
[9] NA Y, LEE H, KWON S. Investigating the effects of long-term contractions on myoelectric recognition of wrist movements from stroke patients[J]. International Journal of Precision Engineering and Manufacturing, 2020, 21: 1771-1779.
[10] FUNDARO C, GAZZONI M, PINNA G D, et al. Is fatigue a muscular phenomenon in Parkinson’s disease? Implications for rehabilitation[J]. European Journal of Physical and Rehabilitation Medicine, 2021, 57: 691-700.
[11] TRIWIYANTO T, WAHYUNGGORO O, NUGROHO H A, et al. Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature[J]. Computers & Electrical Engineering, 2018, 71: 284-293.
[12] HUANG S, CAI S, LI G, et al. sEMG-based detection of compensation caused by fatigue during rehabilitation therapy: a pilot study[J]. IEEE Access, 2019, 7: 127055-127065.
[13] BR?CKLEIN M, IBá?EZ J, BARSAKCIOGLU D Y, et al. Towards human motor augmentation by voluntary decoupling beta activity in the neural drive to muscle and force production[J]. Journal of Neural Engineering, 2021, 18(1): 016001.
[14] LIU X, YANG B, LIANG T, et al. Muscle compensation analysis during motion based on muscle functional network[J]. IEEE Sensors Journal, 2021, 22(3): 2370-2378.
[15] PARK S Y, PARK C H. Diagnosis of muscle fatigue using surface electromyography and analysis of associated factors in type 2 diabetic patients with neuropathy: a preliminary study[J]. International Journal of Environmental Research and Public Health, 2021, 18(18): 9635-9643.
[16] EBENBICHLER G, HABENICHT R, ZIEGELBECKER S, et al. Age-and sex-specific effects in paravertebral surface electromyographic back extensor muscle fatigue in chronic low back pain[J]. GeroScience, 2020, 42(1): 251-269.
[17] KARVEKAR S, ABDOLLAHI M, RASHEDI E. Smartphone-based human fatigue level detection using machine learning approaches[J]. Ergonomics, 2021, 64(5): 600-612.
[18] FANG N, ZHANG C, LV J. Effects of vertical lifting distance on upper-body muscle fatigue[J]. International Journal of Environmental Research and Public Health, 2021, 18(10): 5468.
[19] JI D, HUANG Q. Self-maintenance and automatic identification of the fatigue status of the human body based on Internet of things technology[J]. Sustainable Energy Technologies and Assessments, 2021, 45: 101193.
[20] GONZáLEZ-ZAMORA P, BENITEZ V H, PACHECO J. A feature-based processing framework for real-time implementation of muscle fatigue measurement[J]. Cluster Computing, 2023, 26(1): 385-394.
[21] 姜亚斌, 邹任玲, 刘建. 表面肌电信号的肌肉疲劳判别研究进展[J]. 生物信息学, 2017, 15(2): 120-126.
JIANG Y B, ZOU R L, LIU J. Research progress of muscle fatigue discrimination based on surface electromyography[J]. Chinese Journal of Bioinformatics, 2017, 15(2): 120-126.
[22] SUN J, LIU G, SUN Y, et al. Application of surface electromyography in exercise fatigue: a review[J]. Frontiers in Systems Neuroscience, 2022, 16: 893275.
[23] 付梦龙. 基于表面肌电信号的实时在线肌肉疲劳估计方法研究[D]. 深圳: 中国科学院大学(中国科学院深圳先进技术研究院), 2020.
FU M L. Research on real-time online muscle fatigue estimation method based on surface EMG signal[D]. Shenzhen:University of Chinese Academy of Sciences (Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences), 2020.
[24] 杨森. 基于表面肌电信号的下肢肌肉疲劳分析[D]. 杭州: 杭州电子科技大学, 2022.
YANG S. Fatigue analysis of lower limb muscles based on surface EMG signal[D]. Hangzhou: Hangzhou Dianzi University, 2022.
[25] GOUBAULT E, MARTINEZ R, BOUFFARD J, et al. Shoulder electromyography-based indicators to assess manifestation of muscle fatigue during laboratory-simulated manual handling task[J]. Ergonomics, 2022, 65(1): 118-133.
[26] SHARMA S, KINI A, SHANKAR G, et al. Tensile fractography of artificially aged Al6061-B4C composites[J]. Journal of Mechanical Engineering and Sciences, 2018, 12(3): 3866-3875.
[27] HABETS L E, BARTELS B, DE GROOT J F, et al. Motor unit reserve capacity in spinal muscular atrophy during fatiguing endurance performance[J]. Clinical Neurophysiology, 2021, 132(3): 800-807.
[28] ?ZG?REN N, ARITAN S. Peak counting in surface electromyography signals for quantification of muscle fatigue during dynamic contractions[J]. Medical Engineering & Phy-sics, 2022, 107: 103844.
[29] KIM H, LEE J, KIM J. Electromyography-signal-based muscle fatigue assessment for knee rehabilitation monitoring systems[J]. Biomedical Engineering Letters, 2018, 8: 345-353.
[30] MEHRA P, CHEUNG V C K, TONG R K Y. Muscle endurance time estimation during isometric training using electromyogram and supervised learning[J]. Journal of Electromyography and Kinesiology, 2020, 50: 102376.
[31] DE LA PE?A S, POLO A, ROBLES-ALGARíN C. Implementation of a portable electromyographic prototype for the detection of muscle fatigue[J]. Electronics, 2019, 8(6): 619.
[32] YUN I, JEUNG J, SONG Y, et al. Non-invasive quantitative muscle fatigue estimation based on correlation between sEMG signal and muscle mass[J]. IEEE Access, 2020, 8: 191751-191757.
[33] DE LA FUENTE C, MARTINEZ-VALDES E, PRIEGO-QUESADA J I, et al. Understanding the effect of window length and overlap for assessing sEMG in dynamic fatiguing contractions: a non-linear dimensionality reduction and clus-tering[J]. Journal of Biomechanics, 2021, 125: 110598.
[34] BOYER M, BOUYER L, ROY J S, et al. A real-time algorithm to estimate shoulder muscle fatigue based on surface EMG signal for static and dynamic upper limb tasks[C]//Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Piscataway: IEEE, 2021: 100-106.
[35] RAMPICHINI S, VIEIRA T M, CASTIGLIONI P, et al. Complexity analysis of surface electromyography for assessing the myoelectric manifestation of muscle fatigue: a review[J]. Entropy, 2020, 22(5): 529.
[36] JERO S E, RAMAKRISHNAN S. Analysis of muscle fatigue conditions in surface EMG signal with a novel Hilbert marginal spectrum entropy method[C]//Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Piscataway: IEEE, 2019: 2675-2678.
[37] MURILLO-ESCOBAR J, JARAMILLO MUNERA Y E, ORREGO-METAUTE D A, et al. Muscle fatigue analysis during dynamic contractions based on biomechanical features and permutation entropy[J]. Mathematical Biosciences and Engineering, 2020, 17(3): 2592-2615.
[38] RAVIER P, DáVALOS A, JABLOUN M, et al. The refined composite downsampling permutation entropy is a relevant tool in the muscle fatigue study using sEMG signals[J]. Entropy, 2021, 23(12): 1655.
[39] ZHAO J, SHE J, FUKUSHIMA E F, et al. Muscle fatigue analysis with optimized complementary ensemble empirical mode decomposition and multi-scale envelope spectral entropy[J]. Frontiers in Neurorobotics, 2020, 14: 566172.
[40] WANG S, TANG H, WANG B, et al. Analysis of fatigue in the biceps brachii by using rapid refined composite multiscale sample entropy[J]. Biomedical Signal Processing and Control, 2021, 67: 102510.
[41] CHEN L, ZHANG C, LIU Z, et al. Evaluation of muscle fatigue based on CRP and RQA for upper limb exoskeleton[C]//Proceedings of the 2018 Chinese Automation Congress. Piscataway: IEEE, 2018: 4210-4215.
[42] KAHL L, HOFMANN U G. Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals[J]. Medical Engineering & Physics, 2016, 38(11): 1260-1269.
[43] BERETTA-PICCOLI M, BOCCIA G, PONTI T, et al. Relationship between isometric muscle force and fractal dimension of surface electromyogram[J]. BioMed Research International, 2018: 5373846.
[44] BERETTA-PICCOLI M, D’ANTONA G, BARBERO M, et al. Evaluation of central and peripheral fatigue in the quadriceps using fractal dimension and conduction velocity in young females[J]. PLoS One, 2015, 10(4): e0123921.
[45] MEDURI F, BERETTA-PICCOLI M, CALANNI L, et al. Inter-gender sEMG evaluation of central and peripheral fatigue in biceps brachii of young healthy subjects[J]. PLoS One, 2016, 11(12): e0168443.
[46] TERRACINA D, MONIRI A, RODRIGUEZ-MANZANO J, et al. Real-time forecasting and classification of trunk muscle fatigue using surface electromyography[C]//Proceedings of the 2019 IEEE Biomedical Circuits and Systems Conference. Piscataway: IEEE, 2019: 1-4.
[47] SAMANN F, SCHANZE T. EMG based muscle fatigue detection using autocorrelation and k-means clustering[J]. Proc-eedings on Automation in Medical Engineering, 2023, 2(1): 739-739.
[48] WANG S, TANG H, WANG B, et al. A novel approach to detecting muscle fatigue based on sEMG by using neural architecture search framework[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 34(8): 4932-4943.
[49] CORAGGIO G, CERA M, CIRELLI M, et al. Review and comparison of linear algorithms to quantify muscle fatigue based on sEMG signals[J]. Ergonomics, 2024: 1-19.
[50] QIN P, SHI X. Evaluation of feature extraction and classification for lower limb motion based on sEMG signal[J]. Entropy, 2020, 22(8): 852-869.
[51] PRAVIN D V, RAGAVKUMAR A J, ABINESH S, et al. Extraction, processing and analysis of surface electromyogram signal and detection of muscle fatigue using machine learning methods[C]//Proceedings of the 2023 International Conference on Bio Signals, Images, and Instrumentation. Piscataway: IEEE, 2023: 1-8.
[52] AGUIRRE A, PINTO M J, CIFUENTES C A, et al. Machine learning approach for fatigue estimation in sit-to-stand exercise[J]. Sensors, 2021, 21(15): 5006.
[53] 刘光达, 董梦坤, 张守伟, 等. 基于KPCA-SVM的表面肌电信号疲劳分类研究[J]. 电子测量与仪器学报, 2023, 35(10): 1-8.
LIU G D, DONG M K, ZHANG S W, et al. Research on fatigue classification of surface EMG signal based on KPCA-SVM[J]. Journal of Electronic Measurement and Instrumentation, 2023, 35(10): 1-8.
[54] ZHANG J, LOCKHART T E, SOANGRA R. Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors[J]. Annals of Biomedical Engineering, 2014, 42: 600-612.
[55] KARTHICK P A, GHOSH D M, RAMAKRISHNAN S. Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms[J]. Computer Methods and Programs in Biomedicine, 2018, 154: 45-56.
[56] LIU Q, LIU Y, ZHANG C, et al. sEMG-based dynamic muscle fatigue classification using SVM with improved whale optimization algorithm[J]. IEEE Internet of Things Journal, 2021, 8(23): 16835-16844.
[57] WU Q, MAO J F, WEI C F, et al. Hybrid BF-PSO and fuzzy support vector machine for diagnosis of fatigue status using EMG signal features[J]. Neurocomputing, 2015, 173: 483-500.
[58] WU Q, XI C, DING L, et al. Classification of EMG signals by BFA-optimized GSVCM for diagnosis of fatigue status[J]. IEEE Transactions on Automation Science and Engineering, 2016, 14(2): 915-930.
[59] KHAN T, LUNDGREN L E, J?RPE E, et al. A novel method for classification of running fatigue using change-point segmentation[J]. Sensors, 2019, 19(21): 4729.
[60] PINTO-BERNAL M J, CIFUENTES C A, PERDOMO O, et al. A data-driven approach to physical fatigue management using wearable sensors to classify four diagnostic fatigue states[J]. Sensors, 2021, 21(19): 6401.
[61] MAKARAM N, KARTHICK P A, GOPINATH V, et al. Surface electromyography-based muscle fatigue analysis using binary and weighted visibility graph features[J]. Fluctuation and Noise Letters, 2021, 20(2): 2150016.
[62] DIVYA B K, KARTHICK P A, RAMAKRISHNAN S. Auto-mated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals[J]. Computer Methods in Biomechanics and Biomedical Engineering, 2022, 25(3): 320-332.
[63] WANG Y, LI R, ZHANG Y, et al. Physiological signal analysis in exercise fatigue detection application based on deep learning[EB/OL]. [2023-10-23]. https://doi.org/10.1002/itl2.439.
[64] KALPANA R, VISHALAKSHI T N, SARAVANAN G. A predictive model for the detection of muscle fatigue using sEMG signal[J]. Journal of Survey in Fisheries Sciences, 2023, 10: 3349-3353.
[65] DAI X, LI S. Application analysis of wearable technology and equipment based on artificial intelligence in volleyball[J]. Mathematical Problems in Engineering, 2021(1): 5572389.
[66] WANG J, SUN S, SUN Y. A muscle fatigue classification model based on LSTM and improved wavelet packet threshold[J]. Sensors, 2021, 21(19): 6369.
[67] DANG Y, LIU Z, YANG X, et al. A fatigue assessment method based on attention mechanism and surface electromyography[J]. Internet of Things and Cyber-Physical Systems, 2023, 3: 112-120.
[68] WANG Y, LU C, ZHANG M, et al. Research on the recognition of various muscle fatigue states in resistance strength training[J]. Healthcare. MDPI, 2022, 10(11): 2292.
[69] YANG S, LI M, WANG J, et al. A low-cost and portable wrist exoskeleton using EEG-sEMG combined strategy for prolonged active rehabilitation[J]. Frontiers in Neurorobotics, 2023, 17: 1161187.
[70] WANG J, SUN Y, SUN S. Recognition of muscle fatigue status based on improved wavelet threshold and CNN-SVM[J]. IEEE Access, 2020, 8: 207914-207922.
[71] BALA S, JOSHI D. An attention-based deep CNN-BiLSTM model for forecasting of fatigue-induced surface electromyography signals during isotonic contractions[EB/OL]. [2023- 10-23]. https://doi.org/10.36227/techrxiv.21287487.v1.
[72] LIU J, TAO Q, WU B. Dynamic muscle fatigue state recognition based on deep learning fusion model[J]. IEEE Access, 2023, 11: 95079-95091.
[73] MONIRI A, TERRACINA D, RODRIGUEZ-MANZANO J, et al. Real-time forecasting of sEMG features for trunk muscle fatigue using machine learning[J]. IEEE Transactions on Biomedical Engineering, 2020, 68(2): 718-727.