ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 13 - 2025 | doi: 10.3389/fbioe.2025.1619247
This article is part of the Research TopicIntelligent Rehabilitation Technology Incorporating Multimodal Information Feedback and StimulationView all articles
AI-Driven Hybrid Rehabilitation: Synergizing Robotics and Electrical Stimulation for Upper-Limb Recovery After Stroke
Provisionally accepted
- 1Advanced Technologies in Medicine and Signals (ATMS), Ecole Nationale d’Ingénieurs de Sfax (ENIS), University of Sfax, Sfax 3038, Tunisia, Sfax, Tunisia
- 2Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia, Riadh, Saudi Arabia
- 3Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia., Taif, Saudi Arabia
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This study presents an AI-enhanced hybrid rehabilitation system that integrates a dual-arm robotic platform with electromyography (EMG)-guided neuromuscular electrical stimulation (NMES) to support upper-limb motor recovery in stroke survivors. The system features a symmetrical robotic arm with real-time anatomical adaptation for bilateral therapy and incorporates a Support Vector Machine (SVM)-based model for continuous muscle fatigue detection using time-frequency features extracted from EMG signals. A ROS2-based architecture enables real-time signal processing, adaptive control, and remote supervision by clinicians. The system dynamically adjusts stimulation parameters based on fatigue classification results, allowing personalized and responsive therapy. Preliminary clinical validation with three post-stroke patients demonstrated a 44% increase in range of motion, 45% enhancement in active torque, and 36% reduction in passive torque. The SVM model achieved a 95% accuracy in fatigue detection, and initial patient results suggest the feasibility and potential benefits of this intelligent, closed-loop rehabilitation approach.
Keywords: Upper-limb rehabilitation, machine learning, Electrical Stimulation, Muscle fatigue estimation, support vector machine (SVM), neuromuscular recovery
Received: 27 Apr 2025; Accepted: 16 Jun 2025.
Copyright: © 2025 Ben Abdallah, Bouteraa and Alotaibi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yassine Bouteraa, Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia, Riadh, Saudi Arabia
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