Motion prediction and inverse dynamics for human upper extremities

Santos™, a digital human avatar developed at The University of Iowa, exhibits extensive modeling and simulation capabilities. Santos™ is a part of a virtual environment for conducting human factors analysis consisting of posture prediction, motion prediction, and ergonomics studies. This paper presents part of the functionality in the Santos™ virtual environment, which is an optimization-based algorithm for simulating dynamic motion of Santos™. The joint torque and muscle power during the motion are also calculated within the algorithm. Mathematical cost functions that evaluate human performance are essential to any effort that would evaluate and compare various ergonomic designs. It is widely accepted that the ergonomic design process is actually an optimization problem with many design variables. This effort is basically a task-based approach that believes humans assume different postures and exert different forces to accomplish different tasks. We propose using the concepts of design variables, cost functions, and constraints to formulate the optimization problem for motion/posture prediction. Various human performance measures are currently being reported in research literature as cost functions for motion/posture optimization. Energy consumption is one of the most widely used human performance measures, where total muscle energy is decomposed as mechanical work and heat. An inverse dynamics problem is formulated by minimizing muscle energy cost subject to several physical and physiological constraints to solve for the joint torques, as well as the joint kinematic profiles. The results of the generalized torque at each joint should be useful in future studies of muscle stress prediction with a given task.