Adaptive fuzzy logic restriction rules for error correction and safe stimulation patterns during functional electrical stimulation

Abstract

Adaptive restriction rules based on fuzzy logic have been developed to eliminate errors and to increase stimulation safety in the foot-drop correction application, specifically when using adaptive logic networks to provide a stimulation control signal based on neural activity recorded from peripheral sensory nerve branches. The fuzzy rules were designed to increase flexibility and offer easier customization, compared to earlier versions of restriction rules. The rules developed quantified the duration of swing and stance phases into states of accepting or rejecting new transitions, based on the cyclic nature of gait and statistics on the current gait patterns. The rules were easy to custom design for a specific application, using linguistic terms to model the actions of the rules. The rules were tested using pre-recorded gait data processed through a gait event detector and proved to reduce detection delay and the number of errors, compared to conventional rules.