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A Flexible Wearable Supernumerary Robotic Limb for Chronic Stroke Patients
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Multichannel Optimization Strategy for Functional Electrical Stimulation in Grasp Gesture Restoration: A Pilot Study.

Kong Hoi Cheng, Jinxin Sun, Yuquan Leng

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |June 18, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new modeling approach for functional electrical stimulation (FES) to improve grasping in neurorehabilitation. The method optimizes muscle activation patterns, significantly enhancing grasp accuracy and adaptability for stroke and spinal cord injury patients.

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    Area of Science:

    • Biomedical Engineering
    • Neurorehabilitation
    • Robotics

    Background:

    • Grasping impairments significantly reduce quality of life after stroke or spinal cord injury.
    • Multi-channel functional electrical stimulation (FES) offers potential for motor restoration but faces scalability challenges due to gesture-specific calibration.
    • Restoring hand function is crucial for daily living activities.

    Purpose of the Study:

    • To develop a biophysically informed modeling approach for selecting optimal multichannel FES patterns.
    • To enable a wide range of predictable grasping gestures through advanced modeling.
    • To overcome the limitations of current FES calibration methods.

    Main Methods:

    • Integrated individual-specific muscle characteristics into a bioelectric field model.
    • Quantified spatial interference to inform optimal stimulation pattern selection.
    • Evaluated the system across diverse grasping gestures involving coordinated wrist and finger movements.

    Main Results:

    • The proposed modeling approach effectively distinguished functional stimulation patterns.
    • Achieved a mean grasp accuracy of 0.97 for optimized configurations across all tested gesture categories.
    • Demonstrated effective and adaptable neuromuscular control for various functional grasping tasks.

    Conclusions:

    • The biophysically informed modeling approach enhances FES for grasping in neurorehabilitation.
    • The system shows strong generalizability by adapting to individual physiological characteristics.
    • This approach provides valuable guidance for clinical implementation of FES systems.