Real-time gait event detection using motion capture to control an electrical stimulator: Proof-of-concept
Gabriel Graffagnino, David Gasq, Karine Patte, Benoît Sijobert, Christine Azevedo Coste
公開日: 2025/9/30
Abstract
Cerebral palsy (CP) is the most prevalent motor disorder in childhood and often results in gait abnormalities that hinder mobility and diminish quality of life. Functional electrical stimulation (FES) has demonstrated potential in enhancing gait in individuals in this population, however, its practical implementation remains complex, as it requires monitoring various gait parameters and delivering personalized stimulation to different muscles in order to correct various gait impairments. Recent advancements in real-time motion capture (MOCAP) and wearable sensors now enable the development of closed-loop, multi-channel FES systems. This study will assess the feasibility and responsiveness of a real-time, event-triggered multi-channel stimulation protocol during treadmill walking. The stimulation is triggered by specific gait events (heel strike, knee flexion, ankle dorsiflexion) detected through the MOCAP system and administered via a multichannel electrical stimulator. Conducted on healthy adults, this preliminary study focuses on assessing technical feasibility. We report different technical outcomes including the latency between gait event detection and the appearance of stimulation artifacts in EMG signals. The results confirm the viability of the system, laying the groundwork for future clinical application in the rehabilitation of children with CP.