Human Gait Prediction with a High DOF Upper Body: A Multi-Objective Optimization of Discomfort and Energy Cost

Abstract

To predict the 3D walking pattern of a human, a detailed upper body model that includes the spine, shoulders, and neck must be made, which is challenging because of the coupling relations of degrees of freedom (DOF) in these body sections. The objective of this study was to develop a discomfort function for including a high DOF upper body model during walking. A multi-objective optimization (MOO) method was formulated by minimizing dynamic effort (DE) and the discomfort function simultaneously. The discomfort function is defined as the sum of the squares of deviation of joint angles from their neutral angle positions. The neutral angle position is defined as a relaxed human posture without actively applied external forces. The DE is the sum of the joint torque squared. To illustrate the capability of including a high DOF upper body, backward walking is used as an example. By minimizing both DE and the discomfort function, a 3D whole-body model with a high DOF upper body for walking was simulated successfully. The proposed MOO is a promising human performance measure to predict human motion using a high DOF upper body with full range of motion. This has been demonstrated by simulating backward walking, lifting, and ingress motions.