title: Reliability of neural-network functional electrical stimulation gait-control system.
abstract: 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 stimulation, to replace the function of hand or heel switches. However, there has been no study to evaluate the reliability of these AI systems. Neural networks were used to construct FES controllers to control the timing of stimulation. Different numbers of sensors in the sensor set and different numbers of data points from each sensor were used. Two incomplete-SCI subjects were recruited, and each was tested on three separate occasions. The results 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 provide enough information to construct a reliable FES control system, and the number of data points did not have any effect on the reliability of the system.
authors.affiliation: Bioengineering Unit, University of Strathclyde, Glasgow, UK.
authors.forename: K Y
authors.initials: KY
authors.lastname: Tong
authors.forename: M H
authors.initials: MH
authors.lastname: Granat
mesh_terms: D000328:Adult; D004599:Electric Stimulation Therapy / Q000379:methods*; D005069:Evaluation Studies as Topic; D005260:Female; D005684:Gait; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D013119:Spinal Cord Injuries / Q000534:rehabilitation*