Prediction and analysis of human motion dynamics performing various tasks

TLDR: In this article, an optimisation-based algorithm for simulating the dynamic motion of a digital human is presented, where human performance measures such as the total energy consumption governs human motion; thus, the process of human motion simulation can be formulated as an optimization problem that minimises human performance measure given at different constraints and hand loads.

Abstract: Several digital human softwares have shown the capabilities of simulating simple reach motions. However, predicting the dynamic effects on human motion due to different task loads is still immature. This paper presents an optimisation-based algorithm for simulating the dynamic motion of a digital human. The hypothesis is that human performance measures such as the total energy consumption governs human motion; thus the process of human motion simulation can be formulated as an optimisation problem that minimises human performance measures given at different constraints and hand loads, corresponding to a number of tasks. General equations of motion using Lagrangian dynamics method are derived for the digital human, and human metabolic energy is formulated in terms of joint space. Joint actuator torques and metabolic energy expenditure during motion are formulated and calculated within the algorithm, and it is applied to Santos™, a kinematically realistic digital human, developed at the University of Iowa. Results show that different external loads and tasks lead to different human motions and actuator torque distributions.