Dynamic optimization of walker-assisted FES-activated paraplegic walking: simulation and experimental studies.

In this paper, we propose a musculoskeletal model of walker-assisted FES-activated paraplegic walking for the generation of muscle stimulation patterns and characterization of the causal relationships between muscle excitations, multi-joint movement, and handle reaction force (HRF). The model consists of the lower extremities, trunk, hands, and a walker. The simulation of walking is performed using particle swarm optimization to minimize the tracking errors from the desired trajectories for the lower extremity joints, to reduce the stimulations of the muscle groups acting around the hip, knee, and ankle joints, and to minimize the HRF. The results of the simulation studies using data recorded from healthy subjects performing walker-assisted walking indicate that the model-generated muscle stimulation patterns are in agreement with the EMG patterns that have been reported in the literature. The experimental results on two paraplegic subjects demonstrate that the proposed methodology can improve walking performance, reduce HRF, and increase walking speed when compared to the conventional FES-activated paraplegic walking.