Online CPG-based gait monitoring and optimal control of the ankle joint for assisted walking in hemiplegic subjects

Abstract

The paper introduces an approach to the FES-assisted correction of the drop-foot syndrome in post-stroke hemiplegic patients. The approach is based on a two stage architecture. One stage is dedicated to the online estimation of high-level gait information and the second to the generation of optimal ankle joint trajectories for walking assistance. The general gait information is obtained through the observation of one limb based on a central pattern generator model generating rhythmic trajectories which auto-adapt to real-measurements. This allows us to obtain information about the execution of the walking cycle. Optimal control is used to generate ankle joint dorsi-flexion trajectories during the swing phase of the corresponding deficient leg based on a muscle model and on the information provided by the first stage and some estimated or measured information about the controlled leg. This allows us to minimize a criteria linked to muscle activation, excitation or fatigue while satisfying constraints such as ground clearance, instead of just mimicking a priori chosen foot ankle trajectories which may be suboptimal. The strategy is validated in simulation using experimental data recorded in one healthy subject.