Bipedal gait model for precise gait recognition and optimal triggering in foot drop stimulator: a proof of concept.

Abstract

Electrical stimulators are often prescribed to correct foot drop walking. However, commercial foot drop stimulators trigger inappropriately under certain non-gait scenarios. Past researches addressed this limitation by defining stimulation control based on automaton of a gait cycle executed by foot drop of affected limb/foot only. Since gait is a collaborative activity of both feet, this research highlights the role of normal foot for robust gait detection and stimulation triggering. A novel bipedal gait model is proposed where gait cycle is realized as an automaton based on concurrent gait sub-phases (states) from each foot. The input for state transition is fused information from feet-worn pressure and inertial sensors. Thereafter, a bipedal gait model-based stimulation control algorithm is developed. As a feasibility study, bipedal gait model and stimulation control are evaluated in real-time simulation manner on normal and simulated foot drop gait measurements from 16 able-bodied participants with three speed variations, under inappropriate triggering scenarios and with foot drop rehabilitation exercises. Also, the stimulation control employed in commercial foot drop stimulators and single foot gait-based foot drop stimulators are compared alongside. Gait detection accuracy (98.9%) and precise triggering under all investigations prove bipedal gait model reliability. This infers that gait detection leveraging bipedal periodicity is a promising strategy to rectify prevalent stimulation triggering deficiencies in commercial foot drop stimulators. Graphical abstract Bipedal information-based gait recognition and stimulation triggering.