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<p>This study presents a novel closed-loop bionic hand control system that integrates electromyography (EMG)-driven intent recognition with adaptive neuromuscular electrical stimulation (NMES) to mitigate muscle fatigue and improve user performance. The proposed system features a 3D-printed bionic hand actuated by five independent servomotors, a custom-built electrical stimulator, and a real-time dual-classifier architecture. Muscle fatigue is detected using a Support Vector Machine (SVM) based on frequency-domain EMG features, while handgrip state is classified using a fuzzy logic controller. Experimental trials with 10 neurologically healthy participants demonstrated a 28.6% reduction in muscle fatigue and a 22% improvement in grip force consistency under hybrid control compared to EMG-only operation. The system achieved classification accuracies of 95.4% for fatigue detection and 93% for grip estimation. These results confirm the feasibility of hybrid EMG–NMES systems in enhancing functional performance, stability, and user experience in assistive applications.</p>

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Hybrid EMG–NMES control for real-time muscle fatigue reduction in bionic hands

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

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  • Ismail Ben Abdallah,
  • Yassine Bouteraa,
  • Ahmed Alotaibi

摘要

This study presents a novel closed-loop bionic hand control system that integrates electromyography (EMG)-driven intent recognition with adaptive neuromuscular electrical stimulation (NMES) to mitigate muscle fatigue and improve user performance. The proposed system features a 3D-printed bionic hand actuated by five independent servomotors, a custom-built electrical stimulator, and a real-time dual-classifier architecture. Muscle fatigue is detected using a Support Vector Machine (SVM) based on frequency-domain EMG features, while handgrip state is classified using a fuzzy logic controller. Experimental trials with 10 neurologically healthy participants demonstrated a 28.6% reduction in muscle fatigue and a 22% improvement in grip force consistency under hybrid control compared to EMG-only operation. The system achieved classification accuracies of 95.4% for fatigue detection and 93% for grip estimation. These results confirm the feasibility of hybrid EMG–NMES systems in enhancing functional performance, stability, and user experience in assistive applications.