A Basic Study of Walking Motion Classification Based on Latent Variable Dynamics in Gaussian Process Dynamics Model

Abstract

The deterioration of mobility caused by qualitative deterioration of the locomotor system with aging is called a locomotive syndrome, and the risk of falling increases with the severity of the syndrome. This study aims to develop a motion classification method based on the Gaussian Process Dynamical Model (GPDM) to evaluate gait function and fall risk by measuring daily walking motions. The proposed method probabilistically evaluates the likelihood that a new movement arises from the learned dynamics in GPDM, using a generative model that relates latent variables to body postures and dynamical models that represent the behavior of latent variables.