Control of neuromuscular stimulation for ambulation by complete paraplegics via artificial neural networks.

The paper describes the application of a neural network (ANN) for controlling a functional neuromuscular stimulation (FNS) system to facilitate patient-responsive ambulation by paralyzed patients with traumatic, thoracic-level spinal cord injuries. The particular ANN that is employed is a modified Adaptive-Resonance-Theory (ART-1) network. It serves as a controller in an FNS system (the Parastep system) that is presently in use by approximately 500 patients worldwide (but still without ANN control) and which was the first and only FNS system approved by FDA. The proposed neural network discriminates above-lesion upper-trunk electromyographic (EMG) time series to activate standing and walking functions under FNS and controls FNS stimuli levels using response-EMG signals. For this particular application, several modifications are introduced into the standard ART-1 ANN. First, a modified on-line learning rule is proposed. The new rule assures bi-directional modification of the stored patterns and prevents noise interference. Second, a new reset rule is proposed, which prevents 'exact matching' when the input is a subset of the chosen pattern. A single ART-1-based structure is being applied to solving two problems, namely (1) signal pattern recognition and limb function determination, and (2) control of stimulation levels. This also facilitates ambulation of paraplegics under FNS, 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. The ANN control facilitates continuous update of control settings during normal use, without formal retraining.