Dimensional Compression and Difference Analysis of Sit-to-Stand Motions Using Gaussian Process Dynamics Model

Abstract

Sit-to-stand motion is one of the most crucial activities in daily life because its difficulty can significantly impact the quality of life. Since this action involves various physical characteristics such as muscle strength of the trunk and lower limbs, joint mobility, and sense of balance, its measurement data may contain a great deal of information to evaluate the qualitative deterioration of the locomotor system that may increase the risk of falls and other problems. In this study, we analyze sit-to-stand motion data using the Gaussian Process Dynamics Model (GPDM) to identify and quantify which aspects of the body movements show differences in physical characteristics. GPDM can summarize motion data into low-dimensional latent variables and allows us to examine the effects of physical attributes on movement conditions.