Multichannel Optimization Strategy for Functional Electrical Stimulation in Grasp Gesture Restoration: A Pilot Study

Grasping difficulties profoundly diminish the quality of life in individuals affected by stroke or spinal cord injury. Multi-channel functional electrical stimulation (FES) enables precise, targeted muscle activation through selectively activated electrodes, offering a promising approach for motor restoration in neurorehabilitation. However, the requirement for gesture-specific calibration limits the scalability of the system to the diverse and highly coordinated hand functions essential for daily living. We present a biophysically informed modeling approach that selects optimal multichannel stimulation patterns, enabling an extensive repertoire of predictable grasping gestures. Individual-specific muscle characteristics are integrated into a bioelectric field model, which quantifies spatial interference and informs the selection of optimal stimulation patterns. The system was evaluated across a range of grasping gestures involving coordinated movements of the wrist and finger joints. Experimental evaluation confirmed that the proposed modeling approach effectively distinguishes functional stimulation patterns, yielding a mean grasp accuracy of 0.97 for optimized configurations across all tested gesture categories. These results demonstrate that the proposed approach enables effective and adaptable neuromuscular control across a variety of functional grasping tasks. This approach has shown strong generalizability by adapting to individual physiological characteristics, thereby offering valuable guidance for clinical implementation.