Citation :

Saranyadevi K, Anitha A, C. S D Vijayakumar, R.Praveenkumar, Dharani R, Prathapan B, "Development of a Wireless Sensor-Integrated IoT Framework for EMG-Based Adaptive Muscle Relaxation and Remote Rehabilitation Monitoring," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 21-29, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P103

Abstract

Low-latency, adaptive, and secure bio-signal processing platforms are needed to monitor muscle fatigue and do remote rehabilitation outside the clinical setting. An Internet of Things architecture incorporating a wireless sensor is designed to facilitate adaptive muscle relaxation and remote monitoring of rehabilitation using electromyography. The system incorporates multi-channel surface EMG capture, band-pass filtering 20-450Hz, LMS-based adaptive noise control, RMS and mean frequency feature extraction, as well as an adaptive relaxation index model, all implemented on an ARM Cortex-M4 edge processor with encrypted BLE/Wi-Fi telemetry and cloud analytics. Multi-subject experimental validation results indicate 94.8 percent muscle state classification, 92.1% RMS sensitivity, 4.8% mean frequency estimation error, 1.8% ratio of packet loss, 99.1% cloud uptime, and 110 ms end-to-end latency. The convergence time is attained at 0.72 s with a total efficiency of the system at 96.5%. The architecture allows the biofeedback of remotely located neuromuscular activity, which is provided in real-time in a closed-loop form, and remote neuromuscular monitoring at a scale that supports the adaptive, low-latency rehabilitation intelligence in wearable healthcare settings.

Keywords

Electromyography, Surface Electromyography, Internet of Medical Things, Root Mean Square, Mean Frequency, Inertial Measurement Unit, Bluetooth Low Energy, Advanced RISC Machine.

References

1. Manuela Gomez-Correa, David Cruz-Ortiz, and Mariana Ballesteros, “Wearable and Wireless sEMG Acquisition System based on the Internet of Medical Things,” Sensing and Bio-Sensing Research, vol. 49, pp. 1-10, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Richard Morsch et al., “Enhanced Rehabilitation after Total Joint Replacement using a Wearable High-Density Surface Electromyography System,” Frontiers in Rehabilitation Sciences, vol. 6, pp. 1-10, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
3. M. B. Sudhan et al., “Internet of Things Assisted Sleep Quality Recognition using Hunger Games Search Optimization with Deep Learning on Smart Healthcare Systems,” Journal of Intelligent Systems and Internet of Things, vol. 14, no. 1, pp. 129-140, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Wenhao Liang, “Application of Surface Electromyography (sEMG) in Smart Health Devices,” Academic Journal of Science and Technology, vol. 14, no. 3, pp. 387-390, 2025.
   [CrossRef] [Publisher Link]
5. Nurul Izwani Kamalruzaman, and Muhammad Mahadi Abdul Jamil, “Wireless EMG Monitoring System for Biceps Muscle Recovery,” Evolution in Electrical and Electronic Engineering, vol. 6, no. 2, pp. 1-7, 2025.
   [Google Scholar] [Publisher Link]
6. Jie Yang, Juanjuan Hu, and Wenrui Chen, “IoT-Enabled Real-Time Health Monitoring System for Adolescent Physical Rehabilitation,” Scientific Reports, vol. 15, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Chenyu Tang et al., “Wireless Silent Speech Interface Using Multi-Channel Textile EMG Sensors Integrated into Headphones,” IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-10, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Marie Jose Pérez Peralta et al., “Wireless sEMG-IMU Wearable for Real-Time Squat Kinematics and Muscle Activation,” arXiv Preprint, pp. 1-6, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
9. Sebastian Frey et al., “BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing,” IEEE Transactions on Biomedical Circuits and Systems, pp. 1-17, 2026.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Hussein Naeem Hasan, “A Wearable Rehabilitation System to Assist Partially Hand Paralyzed Patients in Repetitive Exercises,” Journal of Physics: Conference Series, vol. 1279, pp. 1-9, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Bulcha Belay Etana et al., “Integrating Wearable Textile Sensors and IoT for Continuous sEMG Monitoring,” Sensors, vol. 24, no. 6, pp. 1-15, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Anshi Xiong, Tao Wu, and Jingtao Jia, “Design of a Real-Time Monitoring System for Electroencephalogram and Electromyography Signals in Cerebral Palsy Rehabilitation via Wearable Devices,” Electronics, vol. 13, no. 15, pp. 1-20, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Ngoc-Khoat Nguyen et al., “An sEMG Signal-Based Robotic Arm for Rehabilitation Applying Fuzzy Logic,” Enginnering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14287-14294, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Dohyung Kim, JinKi Min, and Seung Hwan Ko, “Recent Developments and Future Directions of Wearable Skin Biosignal Sensors,” Advanced Sensor Research, vol. 3, no. 2, pp. 1-22, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Vijayalakshmi Sankaran et al., “An Internet of Medical Things-based Smart Electromyogram Device for Monitoring of Musculoskeletal Disorders,” Engineering Proceedings, vol. 82, no. 1, pp. 1-10, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Ilaria Siviero et al., “Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics,” 2024 46^th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, pp. 1-4, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Muhammad Al-Ayyad et al., “Electromyography Monitoring Systems in Rehabilitation: Clinical Applications, Wearable Devices and Signal Acquisition Methodologies,” Electronics, vol. 12, no. 7, pp. 1-35, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Fatma M. Talaat, and Rana Mohamed El-Balka, “Stress Monitoring Using Wearable Sensors: IoT Techniques in Medical Field,” Neural Computing and Applications, vol. 35, pp. 18571-18584, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
19. Avenaash R. S et al., “EMG Monitoring using Internet of Things,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 11, no. 3, pp. 128-132, 2023.
    [CrossRef]  [Publisher Link]
20. Eion Tyacke et al., “From Unstable Contacts to Stable Control: A Deep Learning Paradigm for HD-sEMG in Neurorobotics,” arXiv preprint, pp. 1-6, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]