Abstract
Synthetic signals that represent fatiguing contractions of biceps brachii muscle are generated in this work using a comprehensive mathematical model. These signals are the biomarkers of muscle electrical activity that could be recorded non-invasively on the skin surface using Surface Electromyography (sEMG). The important components of the adopted synthetic sEMG model are current source, volume conductor, motor unit recruitment, and firing behavior functions. For this study, the amplitude (A) and scaling factor (λ) of the current source function is selected appropriate to fatiguing conditions. Further, tunable Q-wavelet method is applied to compute the frequency range associated with fatigue in the synthetic signal. The resultant wavelet coefficients are obtained using multirate filter bank where the scaling factors α and β are chosen so as to meet the anticipated Q-factor and the ranges of frequency bands. The results show that synthetically generated signal is able to truly represent fatiguing and nonfatiguing conditions. The amplitude-based features of tunable Q-wavelet coefficients are able to identify the characteristic changes associated with varied fatiguing conditions. Model generated frequency responses in fatiguing conditions are in agreement with the experimental results reported elsewhere. As fatigue is a temporary failure of skeletal muscles to maintain a required force for the accomplishment of a particular task, the model proposed here could be used as a validation of sEMG measurements in health and disease.
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References
Cifrek, M., Medved, V., Tonković, S., Ostojić, S.: Surface EMG based muscle fatigue evaluation in biomechanics. Clin. Biomech. 24(4), 327–340 (2009)
Merletti, R., Parker, P.A.: Electromyography: physiology, engineering, and non-invasive applications. Wiley, New Jersey, USA (2004)
Zwarts, M.J., Bleijenberg, G., Van Engelen, B.G.M.: Clinical neurophysiology of fatigue. Clin. Neurophysiol. 119(1), 2–10 (2008)
Massó, N., Rey, F., Romero, D., Gual, G., Costa, L., Germán, A.: Surface Electromyography Applications. Apunt. Med. L’Esport. 45(166), 127–136 (2010)
Cao, H., Boudaoud, S., Marin, F., Marque, C.: Surface EMG-force modelling for the biceps brachii and its experimental evaluation during isometric isotonic contractions. Comput. Methods Biomech. Biomed. Eng. 18(9), 1014–1023 (2015)
Venugopal, G., Deepak, P., Ghosh, D.M., Ramakrishnan, S.: Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm. Part H: J. Eng. Medi. 231(11), 1025–1033 (2017)
Petersen, E., Rostalski, P.A.: Comprehensive mathematical model of motor unit pool organization, surface electromyography, and force generation. Front. Physiol. (2019)
Stegeman, D.F., Blok, J.H., Hermens, H.J., Roeleveld, K.: Surface EMG models: properties and applications. J. Electromyogra. Kinesiol. 10(5), 313–326 (2000)
Fuglevand, A.J., Winter, D.A., Patla, A.E., Stashuk, D.: Detection of motor unit action potentials with surface electrodes: influence of electrode size and spacing. Biol. Cybern. 67, 143–153 (1992)
Fuglevand, A.J., Winter, D.A., Patla, A.E.: Models of recruitment and rate coding organization in motor-unit pools. J. Neurophysiol. 70(6), 2470–2488 (1993)
Wheeler, K.A., Kumar, D.K., Shimada. H.: An accurate bicep muscle model with sEMG and muscle force outputs. J. Med. Biol. Eng. 30(6), 393–398 (2010)
Rannou, F., Nybo, L., Andersen, J.E., Nordsborg, N.B.: Monitoring muscle fatigue progression during dynamic exercise. Med. Sci. Sports Exerc. 51(7), 1498–1505 (2019)
Celichowski, J., Grottel, K., Rakowska, A.: Changes in motor unit action potentials during the fatigue test. Acta. Neurobiol. Exp. 51, 145–155 (1991)
Caesarendra, W., Tjahjowidodo, T.: A review of feature extraction methods in vibration-based condition monitoring and its application for degradation trend estimation of low-speed slew bearing. Machines 5(4), 21 (2017)
Phinyomark, A., Limsakul, C., Phukpattaranont, P.: A novel feature extraction for robust EMG pattern recognition. J. Comput. 1(1), 71–80 (2009)
Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 39, 7420–7431 (2012)
Bonato, P., Roy, S.H., Knaflitz, M., De Luca, C.J.: Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions. IEEE Trans. Biomed. Eng. 48(7), 745–753 (2001)
Karthick, P.A., Ramakrishnan, S.: Surface electromyography based muscle fatigue progression analysis using modified B distribution time–frequency features. Biomed. Signal Process. Control 26, 42–51 (2016)
Selesnick, I.W.: Wavelet transform with tunable Q-factor. IEEE Trans. Signal Process. 59(8), 3560–3575 (2011)
Abdel-Ghaffar, E.A.: Effect of tuning TQWT parameters on epileptic seizure detection from EEG signals. In: Proceedings of IEEE 12th International Conference on Computer Engineering and Systems (ICCES), pp. 47–51. Egypt (2017).
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Hari, L.M., Jero, S.E., Venugopal, G., Ramakrishnan, S. (2021). Model-Based Simulation of Surface Electromyography Signals and Its Analysis Under Fatiguing Conditions Using Tunable Wavelets. In: Saha, S.K., Mukherjee, M. (eds) Recent Advances in Computational Mechanics and Simulations. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-8315-5_9
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