Estimating joint moments of stand-up type PMV drivers with and without intention during turning by multiple regression analysis

Abstract

In this study, we researched correlations between the joint moments and the variables obtained from the experimental data to estimate the driver's joint moments in order to simulate the driver's behavior during turning considering the driver's intention when riding on a stand-up type Personal Mobility Vehicle (PMV). First, we measured human behavior, handle reaction force, and floor reaction force using motion capture, 6-axis force sensor, reaction force meter attached to the foot. From the obtained external forces and human behavior, the driver’s joint moments were calculated using inverse kinematic analysis and inverse dynamic analysis. The loads on the joints during the turn was examined, and it was found that the loads were greater in the shoulder, hip and ankle joints. Therefore, correlations such as external forces, vehicle velocity, and joint angle to the joint moments of the shoulder, hip, and ankle joints were investigated. As a result, it was found that the correlation with external force was high. Accordingly, the joint moments of the shoulder, hip and ankle joints were estimated by linear multiple regression analysis using the external force. The joint moments of the shoulder and ankle joints were estimated with good accuracy, but those of the hip joint were inaccurate. From these results, it can be interpreted that the joint moments of the shoulder and ankle joints depend on the external force applied to the hands and feet.

Keywords

• personal mobility vehicle (pmv)
• joint moment
• motion capture
• correlation analysis
• inverse dynamic analysis
• regression analysis