Neural network control of FES in paraplegics for patient-responsive ambulation

The paper describes an ART-1-based neural network adapted for controlling functional electrical stimulation (FES) to facilitate patient-responsive ambulation by paralyzed patients with spinal cord injuries. The proposed neural network discriminates above-lesion upper-trunk EMG time series to activate standing and walking functions under FES and controls FES stimuli levels using response-EMG signals. For this particular application we introduce several modifications of ART-1 theory for pattern recognition and classification. We show the applicability of a single ART-1-based structure to solving two problems: (a) signal pattern recognition and classification and (b) control. This also facilitates ambulation of paraplegics under FES, with adequate patient interaction in initial system training, retraining the network when needed, and in allowing patient´s manual over-ride in the case of error, where any manual over-ride serves as a re-training input to the neural network