DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

In this study, we proposed a novel machine-learning-based functional electrical stimulation (FES) control algorithm to enhance gait rehabilitation in post-stroke hemiplegic patients. The electrical stimulation of the muscles on the paretic side was controlled via deep neural networks, which were trained using muscle activity data from healthy people during gait. The performance of the developed system in comparison with that of a conventional FES control method was tested with healthy human subjects.

Automated summary

A novel machine-learning-based FES control algorithm was proposed in this study to enhance gait rehabilitation in post-stroke hemiplegic patients. The system controlled electrical stimulation via deep neural networks trained using muscle activity data from healthy people during gait, and was tested with healthy human subjects in comparison with a conventional FES control method.