DEVELOPMENT OF AN IOT-ENABLED MYOELECTRIC SIGNAL MONITORING SYSTEM AS AN INTERACTIVE INSTRUCTIONAL TOOL FOR ELECTRICAL ENGINEERING EDUCATION
DOI:
https://doi.org/10.55123/storage.v5i2.8100Keywords:
Electromyography, EMG, IoT, Biomedical, Media PembelajaranAbstract
This study discusses the development of an Internet of Things (IoT)-based myoelectric signal monitoring system using Electromyography (EMG) as an interactive learning medium in biomedical instrumentation education. The proposed system utilizes an EMG sensor to capture muscle activity signals, while an ESP8266 microcontroller transmits the acquired data in real time to the Blynk platform for visualization and monitoring purposes. Experimental results demonstrate that the system operates properly and exhibits a strong linear response between load variation and EMG signal voltage. Validation conducted by media experts and testing involving university students indicate that the developed learning media is highly feasible for educational use, achieving an average feasibility score above 90% and a usability score of 71.5 out of 75. Despite these positive results, several improvements are recommended for future development. These include enhancing electrode quality and adding shielding to the sensor cables to minimize electromagnetic interference. Furthermore, future studies may integrate data logging features for long-term analysis and implement machine learning algorithms for automatic muscle activity pattern classification. The integration of these features is expected to improve the functionality and applicability of the proposed system in engineering and biomedical education.
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Abdulmalek, S. et al. (2022) “IoT-Based Healthcare-Monitoring System Towards Improving Quality of Life: A Review,” Healthcare, 10(10), p. 1993. Available at: https://doi.org/10.3390/healthcare10101993.
Ahsan, M.M. and Siddique, Z. (2022) “Industry 4.0 in Healthcare: A systematic review,” International Journal of Information Management Data Insights, 2(1), p. 100079. Available at: https://doi.org/10.1016/j.jjimei.2022.100079.
Al-kahtani, M.S., Khan, F. and Taekeun, W. (2022) “Application of Internet of Things and Sensors in Healthcare,” Sensors, 22(15), p. 5738. Available at: https://doi.org/10.3390/s22155738.
Greenwood, D.A. et al. (2015) “Overcoming Clinical Inertia: A Randomized Clinical Trial of a Telehealth Remote Monitoring Intervention Using Paired Glucose Testing in Adults With Type 2 Diabetes,” Journal of Medical Internet Research, 17(7), p. e178. Available at: https://doi.org/10.2196/jmir.4112.
Hassan, A. and Tapper, E.B. (2025) “Patient-Centered Treatment of Cirrhosis,” Seminars in Liver Disease [Preprint]. Available at: https://doi.org/10.1055/a-2684-6289.
Karthickraja, R. et al. (2020) “COVID-19 Prediction and Symptom Analysis Using Wearable Sensors and IoT,” International Journal of Pervasive Computing and Communications, 18(5), pp. 499–507. Available at: https://doi.org/10.1108/ijpcc-09-2020-0146.
Li, W., Shi, P. and Yu, H. (2021) “Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future,” Frontiers in Neuroscience, 15. Available at: https://doi.org/10.3389/fnins.2021.621885.
Lowndes, B.R. et al. (2017) “Iterative Implementation of a Remote Cardiac Patient Monitoring Device Using Qualitative Analysis and Human Factors Engineering,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), pp. 1081–1085. Available at: https://doi.org/10.1177/1541931213601874.
Moh Jauhariyanto, Farihin Lazim and Achmad Baijuri (2025) “IMPLEMENTASI AUTOMATIC FISH FEEDER DI KERAMBA MENGGUNAKAN MIKROKONTROLER,” STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer, 4(3), pp. 242–249. Available at: https://doi.org/10.55123/storage.v4i3.5853.
Morales-Botello, M.L. et al. (2021) “Chronic Patient Remote Monitoring Through the Application of Big Data and Internet of Things,” Health Informatics Journal, 27(3). Available at: https://doi.org/10.1177/14604582211030956.
Patel, S. et al. (2012) “A review of wearable sensors and systems with application in rehabilitation,” Journal of NeuroEngineering and Rehabilitation. Available at: https://doi.org/10.1186/1743-0003-9-21.
Qi, K. (2025) “Advancing Hospital Healthcare: Achieving IoT-based Secure Health Monitoring Through Multilayer Machine Learning,” Journal of Big Data, 12(1). Available at: https://doi.org/10.1186/s40537-024-01038-w.
Sakib, S. et al. (2022) “Deep Learning Models for Magnetic Cardiography Edge Sensors Implementing Noise Processing and Diagnostics,” Ieee Access, 10, pp. 2656–2668. Available at: https://doi.org/10.1109/access.2021.3138976.
Sarhan, S.M., Al-Faiz, M.Z. and Takhakh, A.M. (2023) “A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients,” Heliyon, 9(8), p. e18308. Available at: https://doi.org/10.1016/j.heliyon.2023.e18308.
Stamenova, V. et al. (2020) “Technology-Enabled Self-Management of Chronic Obstructive Pulmonary Disease With or Without Asynchronous Remote Monitoring: Randomized Controlled Trial,” Journal of Medical Internet Research, 22(7), p. e18598. Available at: https://doi.org/10.2196/18598.
Uppal, M. et al. (2022) “Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning,” Sustainability, 14(18), p. 11667. Available at: https://doi.org/10.3390/su141811667.
Wilson, S. and Vaidyanathan, R. (2017) “Upper-limb prosthetic control using wearable multichannel mechanomyography,” 2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, pp. 1293–1298. Available at: https://doi.org/10.1109/ICORR.2017.8009427.
Yen, J.M. and Lim, J.H. (2023) “A Clinical Perspective on Bespoke Sensing Mechanisms for Remote Monitoring and Rehabilitation of Neurological Diseases: Scoping Review,” Sensors, 23(1), p. 536. Available at: https://doi.org/10.3390/s23010536.
Zhou, J., Ho, V. and Javadi, B. (2022) “New Internet of Medical Things for Home-Based Treatment of Anorectal Disorders,” Sensors, 22(2), p. 625. Available at: https://doi.org/10.3390/s22020625.
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