Analysis of EMG and ECG Signals on the Muscles of Water Polo Athletes using MLPNN, A cognitive, Practical and Rehabilitation Approach
Main Article Content
Keywords
Electromyogram (EMG), Electrocardiogram (ECG), ANN, Filtering, Water Polo Athletes, Sport
Abstract
Today, a large number of athletes perform many tests to increase the performance of their abilities related to muscle strength. One of these tests is to examine their electromyogram signal or EMG for short. In this research, advanced processes for evaluating the feature space for water polo athletes have been studied. To support the aforementioned issue, we have recorded the EMG and ECG signals from 20 athletes. For this purpose, after recording the the EMG from the muscles of Trapezius, Pectoralis, Deltoid, Triceps as well as signal processing and feature extraction, the feature space is evaluated, and finally, using MLP neural network, we have classified these four muscles for practical applications applied for Rehabilitation Purposes. After that, we first distinguished the two motion classes, Flexion & Extension, from each other, and then differentiated the extension, supination, and pronation movements from each other and then examined the third channel called the goniometer. Then, after filtering the processed data and using the KNN classifier, the percentage of accuracy of the MLP neural network used in this study demonstrated 89.93%, which indicates that using the EMG signal for athletes, the performance of the damaged muscle as well as enhancement regarding rehabilitation in cases of injuries has improved and can be expected
References
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E. Chandra Sekaran. (4104). Design and Development of EMG Controlled Prosthetics Limb, Procedia Engineering, Vol. 33, 3229–3220.
Xuance Zhou , Carmel Majidi, Oliver M. O’Reilly. (4102). Improving Industrial Design through Handson Experimentation. International Journal of Solids and Structures, Vol. 62–62, 022–062.
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Bagheri, I., Alizadeh, S., & Irankhah, E. Design and Implementation of Wireless IMU-based Posture Correcting Biofeedback System. (2020).
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Aurell, Erik, et al. "Refined second law of thermodynamics for fast random processes." Journal of statistical physics 147.3 (2012): 487-505.
G. Gudnason, E. Bruun, and M. Haugland, “A chip for an implantable neural stimulator,” . 2000.
K. Arabi and M. Sawan, “Implantable multiprogrammable microstimulator dedicated to bladder control,” Medical and Biological Engineering and Computing, 1996.
Q. Xu, J. Li, W. Han, and H. Zhou, “A fully implantable stimulator with wireless power and data transmission for experimental use in epidural spinal cord stimulation,” IEEE EMBS, 2011
V. Valente, A. Demosthenous, and R. Bayford, “A tripolar currentsteering stimulator ASIC for field shaping in deep brain stimulation,” IEEE .2012.
H. McDermott, “An advanced multiple channel cochlear implant,” IEEE . 1989.
L. S. Y. Wong, S. Hossain, A. Ta, J. Edvinsson, D. H. Rivas, and H. Naas, “A very low-power CMOS mixed-signal IC for implantable pacemaker applications,” IEEE .2004.
M. Ortmanns, A. Rocke, M. Gehrke, and H. Tiedtke, “A 232-channel epiretinal stimulator ASIC,” IEEE .2007.
M. Ghovanloo and K. Najafi, “A wireless implantable multichannel microstimulating systemon-a-chip with modular architecture,” IEEE , 2007.
M. Sivaprakasam, W. T. Liu, M. S. Humayun, and J. D. Weiland, “A variable range bi-phasic current stimulus driver circuitry for an implantable retinal prosthetic device,” IEEE , 2005.
N. Dommel, Y. T. Wong, T. Lehmann, C. W. Dodds, N. H. Lovell, and G. J. Suaning, “A CMOS retinal neurostimulator capable of focused, simultaneous stimulation,” Journal of Neural Engineering, 2009.
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A. Z. Alex and T. Lehmann, “Highly efficient AC power transfer in distributed biomedical implants using silicon-on-sapphire technology,” IEEE , 2011, 61
S. Rajapandian, K. L. Shepard, P. Hazucha, , and T. Karnik, “Highvoltage power delivery through charge recycling,” IEEE , 2006.
Ehrmann, G., Blachowicz, T., Homburg, S. V., & Ehrmann, A. (2022). Measuring Biosignals with Single Circuit Boards. Bioengineering, 9(2), 84.
Y. Yang and T. Lehmann, “Current recycling in linear regulators for biomedical implants,” IEEE , 2010,
M. Ghovanloo and K. Najafi, “A high-rate frequency shift keying demodulator chip for wireless biomedical implants,” IEEE , 2003,
L. H. Jung, P. Byrnes-Preston, R. Hessler, T. Lehmann, G. J. Suaning, and N. H. Lovell, “Dual band wireless power and FSK data telemetry for biomedical implants,” 29th IEEE , 2007
L. H. Jung, T. Lehmann, G. J. Suaning, and N. H. Lovell, “A novel semistatic thresholdtriggered delay element for low power applications,” in IEEE , 2011
S. Atluri and M. Ghovanloo, “Incorporating back telemetry in a fullwave CMOS rectifier for RFID and biomedical applications,” IEEE , 2007
D. R. Merrill, M. Bikson, and J. G. R. Jefferys, “Electrical stimulation of excitable tissue” Journal of Neuroscience Methods, , 2005.
H. Chun, T. Lehmann, and Y. Yang, “Implantable stimulator for bipolar stimulation without charge balancing circuits,” IEEE , 2010.
Y. Moghe and T. Lehmann, “A novel safety system concept and implementation for implantable stimulators: A universal DC tissue leakage current detector,” IEEE , 2008
M. Ghovanloo and S. Atluri, “A Wide-Band Power-Efficient Inductive Wireless Link for Implantable Microelectronic Devices Using Multiple Carriers,” IEEE ، 2007.
U.-M. Jow and M. Ghovanloo, “Design and Optimization of Printed Spiral Coils for Efficient Transcutaneous Inductive Power Transmission,” IEEE ، 2007.
K. M. Silay, C. Dehollain, and M. Declercq, “Improvement of Power Efficiency of Inductive Links for Implantable Devices,” , 2008.
T. H. Lee, The Design of CMOS Radio-Frequency Integrated Circuits, Cambridge University Press, 2004.
H. M. Greenhouse, “Design of Planar Rectangular Microelectronic Inductors,” IEEE،1974.
K. Kang, J. Shi, W. Y. Yin, L. W. Li, S. Zouhdi, S. C. Rustagi and K. Mouthaan, “Analysis of Frequency- and Temperature-Dependent Substrate Eddy Currents in On-chip Spiral Inductors Using the Complex Image Method,” IEEE ،2007.
Y. Eo and W. R. Eisenstadt, “High-speed VLSI interconnect modeling based on S-parameter measurements,” IEEE . 1993.
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Nov 4, 2023
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Mahdi Bayat, Faeze Molaee, Iman Bagheri. (2023). Analysis of EMG and ECG Signals on the Muscles of Water Polo Athletes using MLPNN, A cognitive, Practical and Rehabilitation Approach . Journal of Population Therapeutics and Clinical Pharmacology, 30(14), (430-445. https://doi.org/10.53555/jptcp.v30i14.3322
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All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Mahdi Bayat, Faeze Molaee, Iman Bagheri. (2023). Analysis of EMG and ECG Signals on the Muscles of Water Polo Athletes using MLPNN, A cognitive, Practical and Rehabilitation Approach . Journal of Population Therapeutics and Clinical Pharmacology, 30(14), (430-445. https://doi.org/10.53555/jptcp.v30i14.3322