Gait control system for functional electrical stimulation using neural networks

In functional electrical stimulation (FES) systems for restoring walking in spinal cord injured (SCI) individuals, hand switches are the preferred met hod for controlling stimulation timing. Through practice the user becomes a n 'expert' in determining when stimulation should be applied. Neural networ ks have been used to 'clone' this expertise but these applications have use d small numbers of sensors, and their structure has used a binary output, g iving rise to possible controller oscillations. It was proposed that a thre e-layer structure neural network with continuous function, using a larger n umber of sensors, including 'virtual' sensors, can be used to 'clone' this expertise to produce good controllers. Using a sensor set of ten force sens ors and another of 13 'virtual' kinematic sensors, a good FES control syste m was constructed using a three-layer neural network with five hidden nodes . The sensor set comprising three sensors showed the best performance. The accuracy of the optimum three-sensor set for the force sensors and the virt ual kinematic sensors was 90% and 93%, respectively, compared with 81% and 77% for a heel switch. With 32 synchronised sensors, binary neural networks and continuous neural networks were constructed and compared. The networks using continuous function had significantly fewer oscillations. continuous neural networks offer the ability to generate good FES controllers.