Automatic identification of gait events using aninstrumented sock

Abstract

Background: textile-based transducers are an emerging technology in which piezo-resistive properties of materialsare used to measure an applied strain. By incorporating these sensors into a sock, this technology offers thepotential to detect critical events during the stance phase of the gait cycle. This could prove useful in severalapplications, such as functional electrical stimulation (FES) systems to assist gait.Methods: we investigated the output of a knitted resistive strain sensor during walking and sought to determinethe degree of similarity between the sensor output and the ankle angle in the sagittal plane. In addition, weinvestigated whether it would be possible to predict three key gait events, heel strike, heel lift and toe off, with arelatively straight-forward algorithm. This worked by predicting gait events to occur at fixed time offsets fromspecific peaks in the sensor signal.Results: our results showed that, for all subjects, the sensor output exhibited the same general characteristics asthe ankle joint angle. However, there were large between-subjects differences in the degree of similarity betweenthe two curves. Despite this variability, it was possible to accurately predict gait events using a simple algorithm.This algorithm displayed high levels of trial-to-trial repeatability.Conclusions: this study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance