Comparison of two Wireless Electromyography Sensor Module Designs using wet electrodes and dry electrodes at the time of Sitting motion to stand
One of the biosignals used to identify muscle signals in humans is electromyography. Electromyography signals are frequently utilized as input and are designed to aid in post-stroke therapy recovery or to assist people with disabilities. This phenomena has led to the development of numerous electromyography module sensor designs for use in support of various research-based applications. In this study, an electromyography sensor module without an electrode cable is compared to an electromyography sensor module that uses gel electrodes, plate electrodes, electrode cables, and other electrode technologies. A function generator is used to test each module, and the correlation value is sought to determine the connection between the two modules under consideration. Later, the findings of this study served as the foundation for other studies. Researchers also wish to explore the possibility of developing an electromyography sensor module by altering the wireless EMG sensor module's structure and design. Whereas this study can subsequently be extremely helpful to improve the standing of the Health Poltekkes Kemenkes Surabaya.
E. Farago, S. Chinchalkar, D. J. Lizotte, and A. L. Trejos, “Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients,” pp. 1–15, 2019, doi: 10.3390/s19153309.
J. A. Ruvalcaba, M. I. Gutiérrez, A. Vera, and L. Leija, “Wearable Active Electrode for sEMG Monitoring Using Two-Channel Brass Dry Electrodes with Reduced Electronics,” J. Healthc. Eng., vol. 2020, 2020, doi: 10.1155/2020/5950218.
C. Pylatiuk et al., “Comparison of surface EMG monitoring electrodes for long-term use in rehabilitation device control,” 2009 IEEE Int. Conf. Rehabil. Robot. ICORR 2009, no. May 2014, pp. 300–304, 2009, doi: 10.1109/ICORR.2009.5209576.
A. G. S. Rayo et al., “Design and manufacturing of a dry electrode for EMG signals recording with microneedles,” Adv. Struct. Mater., vol. 72, no. July, pp. 259–267, 2018, doi: 10.1007/978-3-319-59590-0_22.
M. Yamagami et al., “Assessment of dry epidermal electrodes for long-term electromyography measurements,” Sensors (Switzerland), vol. 18, no. 4, pp. 1–15, 2018, doi: 10.3390/s18041269.
A. Paiva, H. Carvalho, A. Catarino, O. Postolache, and G. Postolache, “Development of dry textile electrodes for electromyography a comparison between knitted structures and conductive yarns,” Proc. Int. Conf. Sens. Technol. ICST, vol. 2016-March, pp. 447–451, 2016, doi: 10.1109/ICSensT.2015.7438440.
Y. Fu, J. Zhao, Y. Dong, and X. Wang, “Dry electrodes for human bioelectrical signal monitoring,” Sensors (Switzerland), vol. 20, no. 13, pp. 1–30, 2020, doi: 10.3390/s20133651.
M. S. Rodrigues et al., “Dry electrodes for surface electromyography based on architectured titanium thin films,” Materials (Basel)., vol. 13, no. 9, 2020, doi: 10.3390/ma13092135.
X. Zeng, Y. Dong, and X. Wang, “Flexible electrode by hydrographic printing for surface electromyography monitoring,” Materials (Basel)., vol. 13, no. 10, pp. 1–10, 2020, doi: 10.3390/ma13102339.
Y. Dassonville, C. Barthod, and M. Passard, “Implementation of new dry electrodes and comparison with conventional Ag/AgCl electrodes for whole body electrical bioimpedance application,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2015-Novem, pp. 6864–6867, 2015, doi: 10.1109/EMBC.2015.7319970.
R. G. Scalisi et al., “Inkjet printed flexible electrodes for surface electromyography,” Org. Electron., vol. 18, pp. 89–94, 2015, doi: 10.1016/j.orgel.2014.12.017.
A. M. Naim, K. Wickramasinghe, A. De Silva, M. V. Perera, T. D. Lalitharatne, and S. L. Kappel, “Low-cost Active Dry-Contact Surface EMG Sensor for Bionic Arms,” Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., vol. 2020-Octob, pp. 3327–3332, 2020, doi: 10.1109/SMC42975.2020.9283285.
P. Laferriere, E. D. Lemaire, and A. D. C. Chan, “Surface electromyographic signals using dry electrodes,” IEEE Trans. Instrum. Meas., vol. 60, no. 10, pp. 3259–3268, 2011, doi: 10.1109/TIM.2011.2164279.
Y. M. Chi, T. P. Jung, and G. Cauwenberghs, “Dry-contact and noncontact biopotential electrodes: Methodological review,” IEEE Rev. Biomed. Eng., vol. 3, pp. 106–119, 2010, doi: 10.1109/RBME.2010.2084078.
A. Myers, L. Du, H. Huang, and Y. Zhu, “Novel wearable EMG sensors based on nanowire technology,” 2014 36th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC 2014, pp. 1674–1677, 2014, doi: 10.1109/EMBC.2014.6943928.
E. Vavrinsky et al., “Sensor System for Wireless Bio-Signal Monitoring,” Procedia Chem., vol. 6, pp. 155–164, 2012, doi: 10.1016/j.proche.2012.10.142.
L. Guo, L. Sandsjö, M. Ortiz-Catalan, and M. Skrifvars, “Systematic review of textile-based electrodes for long-term and continuous surface electromyography recording,” Text. Res. J., vol. 90, no. 2, pp. 227–244, 2020, doi: 10.1177/0040517519858768.
E. Lam et al., “Exploring textile-based electrode materials for electromyography smart garments,” J. Rehabil. Assist. Technol. Eng., vol. 9, p. 205566832110619, 2022, doi: 10.1177/20556683211061995.
A. Manuscript et al., “RSC Advances”.
S. H. Yeon et al., “Flexible Dry Electrodes for EMG Acquisition within Lower Extremity Prosthetic Sockets,” Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics, vol. 2020-Novem, pp. 1088–1095, 2020, doi: 10.1109/BioRob49111.2020.9224338.
E. N. Kamavuako, M. Brown, X. Bao, I. Chihi, S. Pitou, and M. Howard, “Affordable embroidered emg electrodes for myoelectric control of prostheses: A pilot study,” Sensors, vol. 21, no. 15, pp. 1–11, 2021, doi: 10.3390/s21155245.
D. Tang et al., “Strain-insensitive elastic surface electromyographic (sEMG) electrode for ecient recognition of exercise intensities,” Micromachines, vol. 11, no. 3, pp. 1–12, 2020, doi: 10.3390/mi11030239.
B. Champaty, P. Dubey, S. Sahoo, S. S. Ray, and K. Pal, “rehabilitation devices,” in International Conference on Magnetics, Machines & Drives (AICERA-2014 iCMMD), 2014, pp. 3–6. doi: 10.1109/AICERA.2014.6908260.
H. Tankisi et al., “Standards of instrumentation of EMG,” Clin. Neurophysiol., vol. 131, no. 1, pp. 243–258, 2020, doi: 10.1016/j.clinph.2019.07.025.
S. S. Lee, K. Y. Shin, and J. H. Mun, “Development of a Preamplifier and a Wireless Surface EMG,” vol. 14, pp. 2748–2751.
Y. Blanc and U. Dimanico, “Electrode Placement in Surface Electromyography (sEMG) ”Minimal Crosstalk Area“ (MCA),” Open Rehabil. J., vol. 3, no. 1, pp. 110–126, 2014, doi: 10.2174/1874943701003010110.
T. George, S. G. K, and K. S. Sivanandan, “Sensing , Processing and Application of EMG signals for HAL ( Hybrid Assistive Limb ),” no. Seiscon, pp. 749–753, 2011.
L. Mesin, R. Merletti, and A. Rainoldi, “Surface EMG: The issue of electrode location,” J. Electromyogr. Kinesiol., vol. 19, no. 5, pp. 719–726, 2009, doi: 10.1016/j.jelekin.2008.07.006.
F. Ali, J. Sintar, M. Aras, and A. Zakishukor, “Design and Construction of 4-DOF EMG-Based Robot Arm System,” no. 12, pp. 669–674, 2019, doi: 10.35940/ijitee.L1116.10812S219.
K. Maeda, E. Konaka, H. Okuda, and T. Suzuki, “The ABC of EMG,” 19th Intell. Transp. Syst. World Congr. ITS 2012, no. April, pp. 1–60, 2012, doi: 10.1016/j.jacc.2008.05.066.
M. Fauzi, E. Yulianto, B. G. Irianto, S. Luthfiyah, and V. Shankhwar, “Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram Monitoring Signals,” vol. 4, no. 3, pp. 114–122, 2022.
J. Kilby and K. Prasad, “Analysis of Surface Electromyography Signals Using Discrete Fourier Transform Sliding Window Technique,” Int. J. Comput. Theory Eng., no. January, pp. 321–325, 2013, doi: 10.7763/ijcte.2013.v5.702.
A. Phinyomark, S. Thongpanja, H. Hu, P. Phukpattaranont, and C. Limsakul, “The Usefulness of Mean and Median Frequencies in Electromyography Analysis,” Comput. Intell. Electromyogr. Anal. - A Perspect. Curr. Appl. Futur. Challenges, 2012, doi: 10.5772/50639.
N. Fahadi, S. Suryono, and D. E. Suseno, “Electromyogram Signal Analysis in Frequency Domain of Uterine Muscle Contraction During Childbirth,” Int. J. Innov. Res. Adv. Eng., vol. 06, no. 06, pp. 2349–2163, 2017, doi: 10.7910/DVN/KGOXNE.
T. Triwiyanto, I. Dewa Gede Hari Wisana, and M. R. Mak’ruf, “Feature extraction and classifier in the development of exoskeleton based on emg signal control: A review,” J. Crit. Rev., vol. 7, no. 12, pp. 879–885, 2020, doi: 10.31838/jcr.07.12.155.
C. Diagram and P. Description, Low Cost Low Power Instrumentation Amplifier. Analog Device, 2011. [Online]. Available: https://www.analog.com/media/en/technical-documentation/data-sheets/AD620.pdf
D. Information, TL07xx Low-Noise JFET-Input Operational Amplifiers, vol. 074. 2017.
ST Microelectronics, “TL071 Low noise JFET single operational amplifier,” Datasheet, no. September, pp. 1–15, 2008.
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