Gait restoration in a spinal cord injured subject via neuromuscular electrical stimulation controlled by an artificial neural network.

Attempts to restore gait in spinal cord injured subjects have stumbled on control difficulties associated to the neuromuscular system's non-linearity and time-variance. Thus, a simple autoadaptive artificial neural network has been devised to control gait swing generation by means of neuromuscular electrical stimulation. Both theoretical and experimental approaches were taken. The computer-based system consisted of a three-layer artificial neural network that read angular data from the hip, knee and ankle joints. The output signal consisted of variations on the applied stimulation pulse width. Surface electrical stimulation was applied to the femoral and peroneal nerves of one leg. Neural network training included off-line supervised learning schemes. The system was tested on a male subject with an incomplete C6-level lesion. Several tests were run to determine whether the off-line trained neural network could correctly control the motion. The effect of on-line learning upon the control performance was also evaluated. The system was found to control the motion with success only at times. Control performance was found to improve in response to the application of on-line learning. Learning stability following on-line learning was found to be satisfactory. In a final test, the artificial neural system had appropriate responses to an initial perturbation, which suggests that further research in this area should be pursued.