ArticleScientific reports2025
Hybrid EMG-NMES control for real-time muscle fatigue reduction in bionic hands.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A hybrid EMG-EEG interface for robust intention detection and fatigue-adaptive control of an elbow rehabilitation robot.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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