ArticleFrontiers in bioengineering and biotechnology2025
AI-driven hybrid rehabilitation: synergizing robotics and electrical stimulation for upper-limb recovery after stroke.
Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Multimuscle Surface-EMG Characterization of Upper-Limb Fatigue During Repetitive Haptic Interaction for Health 5.0 Applications.Bioengineering (Basel, Switzerland) · 2026Article
- Recent advances in electroactive biomaterials and electrical stimulation for skeletal muscle regeneration: materials, strategies, and mechanistic insights.Materials today. Bio · 2026Review
- Redefining Stroke Care with Artificial Intelligence: Recent Advances.Current neurology and neuroscience reports · 2026Review
- Short-Term Facility-Based Functional Electrical Stimulation for Chronic Post-Stroke Foot Drop: A Pilot Study.Bioengineering (Basel, Switzerland) · 2026Article
- The interplay between recovery and immunity in infectious and non-communicable diseases.Frontiers in cellular and infection microbiology · 2026Review
- Ethical Horizons in Robotic Rehabilitation: Ensuring Safe AI Use Under the EU AI Act.Medical sciences (Basel, Switzerland) · 2025Article
- A hybrid EMG-EEG interface for robust intention detection and fatigue-adaptive control of an elbow rehabilitation robot.Scientific reports · 2025Article
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Authors and funding
3 authors.
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Abstract
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.
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Registered trials
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