Ben Abdallah Ismail, Bouteraa Yassine, Alotaibi Ahmed
Advanced Technologies in Medicine and Signals (ATMS), Ecole Nationale d'Ingénieurs de Sfax (ENIS), University of Sfax, Sfax, Tunisia.
King Salman Center for Disability Research, Riyadh, Saudi Arabia.
Front Bioeng Biotechnol. 2025 Jun 25;13:1619247. doi: 10.3389/fbioe.2025.1619247. eCollection 2025.
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.
本研究提出了一种人工智能增强的混合康复系统，该系统将双臂机器人平台与肌电图（EMG）引导的神经肌肉电刺激（NMES）相结合，以支持中风幸存者的上肢运动恢复。该系统具有一个对称的机器人手臂，具有用于双侧治疗的实时解剖适应性，并结合了一个基于支持向量机（SVM）的模型，用于使用从EMG信号中提取的时频特征进行连续肌肉疲劳检测。基于ROS2的架构实现了实时信号处理、自适应控制以及临床医生的远程监督。该系统根据疲劳分类结果动态调整刺激参数，实现个性化和响应式治疗。对三名中风后患者的初步临床验证表明，运动范围增加了44%，主动扭矩增强了45%，被动扭矩降低了36%。SVM模型在疲劳检测中的准确率达到了95%，初步患者结果表明这种智能闭环康复方法的可行性和潜在益处。
