Gait event discrimination using ALNs for control of FES in foot-drop problem

Discrimination of stance and swing phases of the gait is required for control of functional electrical stimulation (FES) used to assist with ankle dorsiflexion in foot-drop problem. Simple thresholds applied to a human whole nerve signal processed using a sophisticated digital signal processing technique did not result in a safe and reliable control method. In this preliminary study, the authors use the same sensory signals to evaluate a gait event discriminator (GED), based on Adaptive Logic Networks (ALNs). The evaluation was performed off-line using neural signals for sensory feedback and a signal from a heel switch as the output to the stimulator. The neural signal was recorded using a cuff electrode implanted around the calcaneal nerve in the left leg of a male subject and the heel switch was installed inside the shoe of the same leg. Preliminary results suggest that ALNs can discriminate precise timing of heel contact and heel lift during FES-assisted walking. Restriction rules based on a priori knowledge were used to verify decisions made by ALNs and to eliminate infrequent functional errors providing maximum safety for the subject.