The Real-time monitoring of muscle fatigue using Surface Electromyography (sEMG)

Authors

  • Imran Qayyum Mundial Department of Mechatronics Engineering NUST College of E&ME, National University of Sciences and Technology NUST Islamabad, 44000, Pakistan.
  • Muhammad Shahzad Alam Khan EME College National University of Science and Technology NUST Islamabad
  • Muhammad Asif School of Chemistry, Lahore College for Women University, Lahore 54000, Pakistan.
  • Faiqa Saheen School of Chemistry, Lahore College for Women University, Lahore 54000, Pakistan.
  • Yasir Ali Department of Computer Science Sir Syed CASE Institue Islamand, Islamabad, 44000, Pakistan.
  • Imad Ali Department of Mechatronics Engineering NUST College of E&ME, National University of Sciences and Technology NUST Islamabad, 44000, Pakistan
  • Akhtar Hussain Phul Khairpur Medical Colleges and University KMC Khairpur Mirs, Sindh, Pakistan.
  • Shakir Sultan Department of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, China.
  • Faisal Rehman EME College National University of Science and Technology NUST Islamabad

Abstract

Muscle fatigue is the decline in muscle performance after undertaking any physical activity. Muscle fatigue can adversely affect the efficiency, productivity, and safety of athletic persons. Monitoring muscle performance during training to avoid any injury and achieve optimum results is a demanding task for current sportsmen. This research discusses the methods of fatiguing muscle and their use in assessing the fatigue of athletes. However, these methods have also been subject to high biases and interrupt athletes’ training. Therefore, this paper aims to monitor real-time muscle fatigue by using electromyogram graphical (EMG) signals to address these concerns. These electrical (signals) impulses vary with fatigue levels, and these EMG signals were acquired from an athlete while lifting different weights (from the forearm muscle). For this research work, we consider a few cases first, the acquired initial signal is amplified, and filtration is applied to reduce signal artifacts. Later, rectification was done before monitoring EMG signals in the time domain. The Muscle exertion scale (BorCR-10 scale) was used for measuring muscle fatigue levels. The number of repartitions with different sizes of weightlifting shows dissimilar results in the development of muscle fatigue. It has been observed that when weight is overloaded compared to human capacity, the precision is quite good compared to accurately and verse visa.

Author Biographies

Muhammad Shahzad Alam Khan, EME College National University of Science and Technology NUST Islamabad

NA

Faisal Rehman, EME College National University of Science and Technology NUST Islamabad

This is Faisal Ul Rehman, His Bachelor's In Electronics From Sukkur IBA University and Digital Fabrication Design Degree from The Center for Bits and Atoms MIT USA. Currently Mr. Faisal is Doing His Post Graduation From China. He does Research on Electronics System Design and Fabrication , Embedded Engineering, Lab on Chip , Bio-Electronics and Sensors , Piezoelectric Sensors and Energy Harvesting Technology , Water Purification and Membrane Technology.

References

Chakrabarti, B., Maity, S., Barui, S. Palbhowmik, S., Das, S., & Neogi, B. (2015). Overview on Literature Survey towards EMG Interpretations Technique in Addition to Several Interdisciplinary Work Related to EMG Interpretations. International Journal of Scientific & Engineering Research, 6(5).

Chowdhury, R. H., Reaz, M. B., Ali, M. A. B. M., Bakar, A. A., Chellappan, K., & Chang, T. G. (2013). Surface electromyography signal processing and classification techniques. Sensors, 13(9), 12431-12466.

de Freitas, M. R., Orsini, M., Prufer de Queiroz Campos Araújo, A., Abraão Jr, L. J., Barbosa, G. M., França, M. C., . . . Trajano, E. (2018). Allgrove syndrome and motor neuron disease. Neurology international, 10(2), 65-68.

De Luca, C. J. (1997). The use of surface electromyography in biomechanics. Journal of applied biomechanics, 13(2), 135-163.

Doerschuk, P. C., Gustafon, D. E., & Willsky, A. S. (1983). Upper extremity limb function discrimination using EMG signal analysis. IEEE Transactions on Biomedical Engineering, (1), 18.

Enoka, R. M., & Duchateau, J. (2008). Muscle fatigue: what, why and how it influences muscle function. The Journal of physiology, 586(1), 11-23.

Farina, D., Merletti, R., Nazzaro, M., & Caruso, I. (2001). Effect of joint angle on EMG variables in leg and thigh muscles. IEEE engineering in medicine and biology magazine, 20(6), 62-71.

Felici, F., Rosponi, A., Sbriccoli, P., Filligoi, G. C., Fattorini, L., & Marchetti, M. (2001). Linear and non-linear analysis of surface electromyograms in weightlifters. European journal of applied physiology, 84(4), 337-342.

Ferrigno, B., Bordett, R., Duraisamy, N., Moskow, J., Arul, M. R., Rudraiah, S., . . . Kumbar, S. G. (2020). Bioactive polymeric materials and electrical stimulation strategies for musculoskeletal tissue repair and regeneration. Bioactive materials, 5(3), 468-485.

Graupe, D., & Cline, W. K. (1975). Functional separation of EMG signals via ARMA identification methods for prosthesis control purposes. IEEE Transactions on Systems, Man, and Cybernetics, (2), 252-259.

Hayashi, M., Tsuchimoto, S., Mizuguchi, N., Miyatake, M., Kasuga, S., & Ushiba, J. (2019). Two-stage regression of high-density scalp electroencephalograms visualizes force regulation signaling during muscle contraction. Journal of neural engineering, 16(5), 056020.

Kumar, A., Pahuja, S., & Singh, A. (2018). Real time monitoring of muscle fatigue and muscle disorder of biceps brachii using Surface Electromyography (sEMG). Paper presented at the 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC).

Kuthe, C. D., Uddanwadiker, R. V., & Ramteke, A. A. (2018). Surface electromyography-based method for computing muscle strength and fatigue of biceps brachii muscle and its clinical implementation. Informatics in Medicine Unlocked, 12, 34-43.

Mambrito, B., & De Luca, C. J. (1984). A technique for the detection, decomposition and analysis of the EMG signal. Electroencephalography and clinical neurophysiology, 58(2), 175-188.

Missenard, O., Mottet, D., & Perrey, S. (2008). The role of cocontraction in the impairment of movement accuracy with fatigue. Experimental brain research, 185(1), 151-156.

Mohr, M., Schön, T., Von Tscharner, V., & Nigg, B. M. (2018). Intermuscular coherence between surface EMG signals is higher for monopolar compared to bipolar electrode configurations. Frontiers in physiology, 9, 566.

Potvin, J. R., & Fuglevand, A. J. (2017). A motor unit-based model of muscle fatigue. PLoS computational biology, 13(6), e1005581.

Reaz, M. B. I., Hussain, M. S., & Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1), 11-35.

Selen, L. P., Beek, P. J., & Van Dieën, J. H. (2005). Can co-activation reduce kinematic variability? A simulation study. Biological cybernetics, 93(5), 373-381.

Shair, E., Ahmad, S. A., Marhaban, M., Mohd Tamrin, S., & Abdullah, A. (2017). EMG processing-based measures of fatigue assessment during manual lifting. BioMed research international, 2017.

Taha, Z., Lee, C. M., Ahamed, N. U., Joseph, S., & Omar, S. F. S. (2016). Performance analysis in strength training: an innovative instrumentation. Procedia engineering, 147, 455-460.

Zhang, Q., Yao, Y., Sun, B., Bai, D., & Yang, J. (2018). Fatigue Detection and Analysis of Upper Limb Muscles. Paper presented at the 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR).

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Published

2022-09-08

Issue

Vol. 6 No. 2 (2022)

Section

Articles

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