Abstract
The human upper body has many degrees of freedom (DoFs) making it highly redundant and allowing for great flexibility during daily tasks. Because of this redundancy, each joint rarely uses its full range of motion (ROM) during activities of daily living (ADL). However, when DoFs are lost due to an amputation, a greater ROM of the intact joints is used. This decreases the number of achievable postures. This paper describes, a kinematic robotic human body model (RHBM) that is used to simulate optimum upper body movements of able-bodied and transradial prosthesis users. The motions of a total of 22 subjects from these two groups were evaluated during five ADLs. The RHBM uses a weighted-least-norm (WLN) solution to bias each joint resulting in a human-like posture. The weights were dynamically updated to prevent the joints from moving beyond the individual's task specific joint limits. These limits were determined by the recorded data of each participant. Applying the individualized joint limits, resulted in postures that were always physically possible for each person and more accurate than the static WLN solution. In the case of prosthesis users, the static WLN solution tended to predict joint angles that were outside of the physical joint limits. The inclusion of joint limit avoidance helped predict more human-like postures.