Abstract
Biological signals play a vital role in monitoring the health condition of the patient. These signals include ECG, EMG, EEG, EKG, etc. Electromyogram is a popular technique to record, evaluate and analyze the electrical activity of the skeletal muscles. Clinically, EMG is used as a diagnostic tool to identify neurological disorders and as an assessment tool in physiotherapy, yoga therapy, rehabilitation, biofeedback, training and sports medicine. Remote monitoring of EMG signals gives us knowledge about muscle activity during exercise or physiotherapy at our convenience thus avoiding muscle fatigue. Commercial EMG acquisition systems are of high cost and maintenance and the wired systems cause discomfort in measuring EMG during rehabilitation exercises. Hence many researchers provided low-cost alternative solutions. In this paper, a detailed survey on different EMG acquisition systems considering the performance, mode of transmission and type of signal conditioning and processing units is done.
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Rahim, A., Forkan, M., Khalil, I.: A probabilistic model for early prediction of abnormal clinical events using vital sign correlations in home-based monitoring. In: Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications, pp. 14–19, Sydney, NSW, Australia (2016)
Halabi, R., El Banna, I., Malaeb, R., Halabi, R., Diab, M.: Novel approach for wireless EMG database collection: applied to muscle building workout routine optimization. In: Fifth International Conference on Advances in Biomedical Engineering (ICABME), pp. 1–4 (2019)
Stastny, P., Gołaś, A., Blazek, D., Maszczyk, A., Wilk, M., Pietraszewski, P., Petr, M., Uhlír, P., Zając, A.: A systematic review of surface electromyography analyses of the bench press movement task. PLoS ONE 12 (2017)
Opar, D.A., Williams, M.D., Shield, A.J.: Hamstring strain injuries: Factors that lead to injury and re-injury. Sports Med. 42(3), 209–226 (2012)
Enoka, R.M., Duchateau, J.: Translating fatigue to human performance. Med. Sci. Sports Exerc. 48(11), 2228–2238 (2016)
Mueller-Wohlfahrt, H.W., Haensel, L., Mithoefer, K., Ekstrand, J., English, B., McNally, S., Orchard, J., van Dijk, C.N., Kerkhoffs, G.M., Schamasch, P., Blottner, D., Swaerd, L., Goedhart, E., Ueblacker, P.: Terminology and classification of muscle injuries in sport: the Munich consensus statement. Br. J. Sports Med. 47(6), 342–350 (2013)
Kellmann, M.: Preventing overtraining in athletes in high-intensity sports and stress/recovery monitoring. Scand. J. Med. Sci. Sports 20(2), 95–102 (2010)
Gränicher, P., Stöggl, T., Fucentese, S.F., et al.: Preoperative exercise in patients undergoing total knee arthroplasty: a pilot randomized controlled trial. Arch. Physiotherapy 10, 13 (2020)
Mabrouk, M.S., Kandil, O.A.: Surface multi-purposes low power wireless electromyography (EMG) system design. Int. J. Comput. Appl. 41(12), 10–16 (2012)
Heaffey, J., Koutsos, E., Georgiou, P.: Live demonstration: wearable device for remote EMG and muscle fatigue monitoring. In: 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–5 (2015)
Liu, S., Lin, C.-B., Chen, Y., Chen, W., Huang, T.-S., Hsu, C.-Y.: An EMG patch for the real-time monitoring of muscle-fatigue conditions during exercise. Sensors 19(14), 3108 (2019)
Yang, Y.-H., Ruan, S.-J., Chen, P.-C., Liu, Y.-T., Hsueh, Y.-H.: A low-cost wireless multichannel surface EMG acquisition system. IEEE Consum. Electron. Mag. 9(5), 14–19 (2020)
Wu, C., Yan, Y., Cao, Q., Fei, F., Yang, D., Song, A.: A low cost surface EMG sensor network for hand motion recognition. In: 2018 IEEE 1st International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS), pp. 35–39 (2018)
Wang, Q., et al.: A high data rate, multi-nodes wireless personal-area sensor network for real-time data acquisition and control. In: First International Conference on Electronics Instrumentation & Information Systems (EIIS), pp. 1–5 (2017)
Ishak, A.J., Ahmad, S.A., Soh, A.C., Naraina, N.A., Jusoh, R.M.R., Chikamune, W.: Design of a wireless surface EMG acquisition system. In: 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 1–6 (2017)
Sayyed, R., Akhter, N., Khan, A., Rabbani, G.: Remote monitoring of EMG signals. Int. J. Res. Anal. Rev. 6(2), 135–141 (2019)
Kledrowetz, V., Prokop, R., Fujcik, L., Pavlík, M., Háze, J.: Low-power ASIC suitable for miniaturized wireless EMG systems. J. Electr. Eng. 70, 393–399 (2019)
Acknowledgements
The study was a part of the project funded by Department of Science and Technology. The authors acknowledge the financial support from Science and Technology of Yoga and Meditation (SATYAM) under the Department of Science and Technology, New Delhi, India for sanctioning the project-File No.: DST/SATYAM/2018/20(G) to Velammal Engineering College, Chennai.
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Delina Rajkumari, R.D., Rajiah, P., Lakshmi Sangeetha, A., Pravin Renold, A., Balaji Ganesh, A. (2022). Wireless sEMG Acquisition and Monitoring—A Survey. In: Satyanarayana, C., Gao, XZ., Ting, CY., Muppalaneni, N.B. (eds) Proceedings of the International Conference on Computer Vision, High Performance Computing, Smart Devices and Networks. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-19-4044-6_3
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DOI: https://doi.org/10.1007/978-981-19-4044-6_3
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