Optimization-Based Dynamic Motion Simulation and Energy Expenditure Prediction for a Digital Human

This paper presents an optimization-based algorithm for simulating the dynamic motion of a digital human. We also formulate the metabolic energy expenditure during the motion, which is calculated within our algorithm. This algorithm is implemented and applied to Santos^™, an avatar developed at The University of Iowa. Santos^™ is a part of a virtual environment for conducting digital human analysis consisting of posture prediction, motion prediction, and physiology studies. This paper demonstrates our dynamic motion algorithm within the Santos^™ virtual environment.

Mathematical evaluations of human performance are essential to any effort to compare various ergonomic designs. In fact, the human factors design process can be formulated as an optimization problem that maximizes human performance. In particular, an optimal design must be found while taking into consideration the effects of different motions and hand loads corresponding to a number of tasks. To evaluate these motions, we propose formulating an optimization problem for motion and posture prediction. Metabolic energy expenditure, where total muscle energy is decomposed as mechanical work and heat, is used to evaluate human performance. Thus, dynamic motion is calculated by minimizing energy expenditure subject to several physical and physiological constraints, then solving for the joint torques and kinematic profiles. The results of the generalized torque at each joint will be useful in future studies of muscle stress prediction during a given task.