Reliability of neural-network functional electrical stimulation gait-control system

Functional electrical stimulation (FES) has been used for restoring walking in spinal-cord injured (SCI) persons. Using artificial intelligence (AI), FES controllers have been developed that allow the automatic phasing of sti mulation, to replace the function of hand or heel switches. However, there has been no study to evaluate the reliability of these Al systems. Neural n etworks were used to construct FES controllers to control the timing of sti mulation. Different numbers of sensors in the sensor set and different numb ers of data points from each sensor were used. Two incomplete-SCI subjects were recruited, and each was tested on three separate occasions. The result s show the neural-network controllers can maintain a high accuracy (around 90% for the two- and three-sensor groups and 80% for the one-sensor group) over a period of six months. Two or three sensors were sufficient to provid e enough information to construct a reliable FES control system, and the nu mber of data points did not have any effect on the reliability of the syste m.