AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1619247/full |
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| author | Ismail Ben Abdallah Ismail Ben Abdallah Yassine Bouteraa Yassine Bouteraa Ahmed Alotaibi Ahmed Alotaibi |
| author_facet | Ismail Ben Abdallah Ismail Ben Abdallah Yassine Bouteraa Yassine Bouteraa Ahmed Alotaibi Ahmed Alotaibi |
| author_sort | Ismail Ben Abdallah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6cf89fa009774dbf8a2e424b38ccfeea |
| institution | Kabale University |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-6cf89fa009774dbf8a2e424b38ccfeea2025-08-20T03:32:15ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-06-011310.3389/fbioe.2025.16192471619247AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after strokeIsmail Ben Abdallah0Ismail Ben Abdallah1Yassine Bouteraa2Yassine Bouteraa3Ahmed Alotaibi4Ahmed Alotaibi5Advanced Technologies in Medicine and Signals (ATMS), Ecole Nationale d’Ingénieurs de Sfax (ENIS), University of Sfax, Sfax, TunisiaKing Salman Center for Disability Research, Riyadh, Saudi ArabiaDepartment of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaKing Salman Center for Disability Research, Riyadh, Saudi ArabiaDepartment of Mechanical Engineering, College of Engineering, Taif University, Taif, Saudi ArabiaKing Salman Center for Disability Research, Riyadh, Saudi ArabiaThis 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.https://www.frontiersin.org/articles/10.3389/fbioe.2025.1619247/fullupper-limb rehabilitationmachine learningelectrical stimulationmuscle fatigue estimationsupport vector machine (SVM)neuromuscular recovery |
| spellingShingle | Ismail Ben Abdallah Ismail Ben Abdallah Yassine Bouteraa Yassine Bouteraa Ahmed Alotaibi Ahmed Alotaibi AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke Frontiers in Bioengineering and Biotechnology upper-limb rehabilitation machine learning electrical stimulation muscle fatigue estimation support vector machine (SVM) neuromuscular recovery |
| title | AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke |
| title_full | AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke |
| title_fullStr | AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke |
| title_full_unstemmed | AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke |
| title_short | AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke |
| title_sort | ai driven hybrid rehabilitation synergizing robotics and electrical stimulation for upper limb recovery after stroke |
| topic | upper-limb rehabilitation machine learning electrical stimulation muscle fatigue estimation support vector machine (SVM) neuromuscular recovery |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1619247/full |
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