Spinal cord injury (SCI) often results in persistent motor deficits that are inadequately addressed by conventional rehabilitation. Transcutaneous spinal cord stimulation (tSCS) is a promising non-invasive neuromodulatory approach that can enhance motor activation and gait performance; however, current tSCS systems rely on static, pre-programmed stimulation parameters that do not adapt to real-time motor output or task demands. This limitation may reduce muscle selectivity and disrupt the spatiotemporal dynamics of spinal network activation required for functional movement.
This study aims to develop and evaluate an AI-powered closed-loop multielectrode tSCS system that dynamically adjusts stimulation parameters in real time based on kinematic and surface electromyography (EMG) feedback during walking in individuals with incomplete SCI. The study will compare immediate muscle recruitment and motor performance between conventional static tSCS and dynamic, targeted tSCS guided by real-time physiological signals.
The investigators hypothesize that AI-driven closed-loop tSCS will be safe and feasible, and will result in superior muscle activation patterns and improved gait performance compared with static stimulation. Findings from this study will provide foundational evidence for adaptive neuromodulation strategies and support the advancement of next-generation, data-driven spinal cord stimulation technologies for neurorehabilitation in SCI.