Visualization and Analysis of Differences in Walking Motions with Gaussian Process Dynamical Model

Abstract

Locomotive syndrome is the decline in mobility function due to aging-related deterioration of the locomotor system, which increases the risk of falling. This study aims to analyze and quantify behavioral components associated with falling risk by measuring daily walking motions. We propose a walking motion analysis method based on the Gaussian Process Dynamics Model (GPDM) to detect changes in body usage. This method compresses gait motion data into a low-dimensional latent variable model using GPDM and visualizes the differences in body movements caused by behavioral conditions through the model’s output.