Development of an IoMT Rehabilitation System with EMG Sensor Integration and Ergonomic Design for Adaptive Medical Rehabilitation
DOI:
https://doi.org/10.62712/ijapset.v1i2.9Keywords:
IoMT Rehabilitation System, Sensor EMG, Smart Rehabilitation Device, Adaptive Rehabilitation, Biomedical Engineering, Ergonomic Design, Embedded System, Real-Time MonitoringAbstract
This study aims to develop an IoMT Rehabilitation System based on the integration of Electromyography (EMG) sensors, embedded Internet of Medical Things (IoMT), adaptive actuators, and ergonomic design to improve the effectiveness of real-time adaptive medical rehabilitation. The study employed a Research and Development (R&D) method using a multidisciplinary approach integrating mechanical engineering, biomedical engineering, and embedded systems. The research subjects consisted of 42 participants, including mechanical engineers, biomedical engineers, rehabilitation physicians, and post-stroke patients with mild to moderate conditions. The research process included mechanical device design, prototype fabrication, EMG and IoMT sensor integration, biomechanical testing, and limited clinical validation. The results showed that the developed rehabilitation system achieved an EMG sensor reading accuracy of 94.2%, improved patient movement efficiency by 28%, and increased device comfort by 31% compared to conventional rehabilitation systems. Furthermore, the implementation of IoMT enabled real-time rehabilitation monitoring through a digital dashboard, allowing medical personnel to evaluate therapy progress more objectively and systematically. The integration of EMG-based adaptive actuators successfully created a closed-loop rehabilitation system capable of providing dynamic movement assistance according to the patient’s physiological conditions. This study contributes to the development of smart rehabilitation engineering based on biomechanics, IoMT, and ergonomic design as a more adaptive, precise, and efficient solution for modern medical rehabilitation.
References
[1] D. Su, Z. Hu, J. Wu, P. Shang, and Z. Luo, “Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition,” Frontiers in Neurorobotics, vol. 17, 2023, doi: 10.3389/fnbot.2023.1186175.
[2] A. A. Hulleck, D. M. Mohan, N. Abdallah, M. El‐Rich, and K. Khalaf, “Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies,” Frontiers in Medical Technology, vol. 4, 2022, doi: 10.3389/fmedt.2022.901331.
[3] D. Vacheva and A. Drumev, “Kinesiological Analysis Using Inertial Sensor Systems: Methodological Framework and Clinical Applications in Pathological Gait,” Sensors, vol. 25, no. 14, p. 4435, 2025, doi: 10.3390/s25144435.
[4] S. Edriss et al., “The Role of Emergent Technologies in the Dynamic and Kinematic Assessment of Human Movement in Sport and Clinical Applications,” Applied Sciences, vol. 14, no. 3, p. 1012, 2024, doi: 10.3390/app14031012.
[5] F. Roggio et al., “Technological advancements in the analysis of human motion and posture management through digital devices,” World Journal of Orthopedics, vol. 12, no. 7, pp. 467–484, 2021, doi: 10.5312/wjo.v12.i7.467.
[6] C. Salchow-Hömmen, M. Skrobot, M. C. E. Jochner, T. Schauer, A. A. Kühn, and N. Wenger, “Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders,” Frontiers in Human Neuroscience, vol. 16, 2022, doi: 10.3389/fnhum.2022.768575.
[7] X. Liu et al., “Wearable Devices for Gait Analysis in Intelligent Healthcare,” Frontiers in Computer Science, vol. 3, 2021, doi: 10.3389/fcomp.2021.661676.
[8] T. Bao et al., “A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022,” Frontiers in Robotics and Ai, vol. 10, 2023, doi: 10.3389/frobt.2023.1265543.
[9] R. I. Hamilton, J. Williams, and C. A. Holt, “Biomechanics beyond the lab: Remote technology for osteoarthritis patient data—A scoping review,” Frontiers in Rehabilitation Sciences, vol. 3, 2022, doi: 10.3389/fresc.2022.1005000.
[10] G. Varrassi, M. L. G. Leoni, A. A. A. Alwany, P. Sarzi‐Puttini, and G. Farì, “Bioengineering Support in the Assessment and Rehabilitation of Low Back Pain,” Bioengineering, vol. 12, no. 9, p. 900, 2025, doi: 10.3390/bioengineering12090900.
[11] P. Bonato, V. Feipel, G. Corniani, G. Arın, and A. Leardini, “Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience,” Gait & Posture, vol. 113, pp. 191–203, 2024, doi: 10.1016/j.gaitpost.2024.06.007.
[12] S. García-de-Villa, D. Casillas-Pérez, A. Jiménez-Martín, and J. J. Garcı́a, “Simultaneous exercise recognition and evaluation in prescribed routines: Approach to virtual coaches,” Expert Systems With Applications, vol. 199, p. 116990, 2022, doi: 10.1016/j.eswa.2022.116990.
[13] A. Senthilvel, B. Sankaragomathi, M. Manju, M. Vijayalakshmi, and K. Sujeeth, “Analysis of Gait Dynamics using Motion and EMG Sensors,” 2024, doi: 10.21203/rs.3.rs-4064168/v1.
[14] F. Dierick, F. Buisseret, and S. Eggermont, “Low-Cost Sensors and Biological Signals,” 2021, doi: 10.3390/books978-3-0365-0843-6.
[15] S. Bhadane, “A Systematic Review of Movement Tracking for Real‐Time Monitoring of Physical Exercises in the Gym,” Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, vol. 15, no. 4, 2025, doi: 10.1002/widm.70057.
[16] M. Asif et al., “Analysis of Human Gait Cycle With Body Equilibrium Based on Leg Orientation,” Ieee Access, vol. 10, pp. 123177–123189, 2022, doi: 10.1109/access.2022.3222859.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Mohamed Wajdi Ladghem-Chikouche, Eka Pandu Cynthia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
