Tip-toe walking detection using CPG parameters from skeleton data gathered by kinect

Abstract

Distinguishing tip-toe walking from normal walking, in human locomotion patterns, becomes important in applications such as Autism disorder identification. In this paper, we propose a novel approach for tip-toe walking detection based on the walk’s Central Pattern Generator (CPG) parameters. In the proposed approach, the tip-toe walking is modeled by a CPG. Then, the motions of subjects are recorded and skeleton data are extracted using the first-generation Microsoft Kinect sensor. The CPG parameters of these motions are determined and compared to the given patterns to distinguish between tip-toe walking and normal walking. The accuracy of classification is promising while further data will improve the accuracy rate.