Identification of Balance States During Walking Using k-NN Based on Plantar Pressure Distribution

Abstract

In this study, we propose a method for identifying walking balance states by using plantar pressure sensors to measure changes in plantar pressure in both balanced and unbalanced walking conditions. Specifically, we collected plantar pressure data during walking and exercise, divided the data into walking cycles, and quantified the intensity of the plantar pressure. Furthermore, we applied a K-(breakpoint)Nearest Neighbor (KNN) model to classify the quantified data into three walking conditions: left foot loaded, right foot loaded, and normal walking. We then evaluated the classification accuracy of this method.