An artificial neural system for closed loop control of locomotion produced via neuromuscular electrical stimulation

The use of neuromuscular electrical stimulation for restoration of gait in spinal cord injured subjects has been seriously pursued by many investigators for the past 15 years. By and large, however, systems to date require the intervention of a person, be it the patient or an observer, and are restricted to control of stimulation onset and termination. Further, existing systems are not adaptable to environmental and patient variations. This work proposes a system that relies on neural computing to determine proper muscle activation patterns from biomechanical signals. The intelligent system is trained to perform gait under supervision, after which it can be used to control muscle stimulation in an unknown environment. Computer simulations suggest that the best neural architecture for control of gait is a neural network including units corresponding to movement history. Separate networks for the stance and swing phases, respectively, were found to work better than a single neural network trained on the entire gait cycle. The artificial neural device proposed here also includes a voice recognition system that will allow for voluntary locomotion. A safety circuit has been designed to preclude acceptance of unwanted vocal commands in the latter system.