Gait Recognition Based on the Geometric Relationship of Acceleration Vectors

Abstract

This study proposes a method that leverages the geometric relationship of acceleration vectors for gait recognition. We use a public dataset in that acceleration sensors were attached to the right pelvis and left thigh of walkers. A low-pass filter based on 95% power distribution was applied to each body part to extract the main motion components. As features, squared magnitudes of the acceleration vectors and dot and cross products of two simultaneously measured acceleration vectors were calculated to represent the characteristics of individual walker. Using these features, a classification experiment was conducted with a Random Forest (RF) algorithm.