Optimization-based posture prediction for human upper body

Abstract

A general methodology and associated computational algorithm for predicting postures of the digital human upper body is presented. The basic plot for this effort is an optimization-based approach, where we believe that different human performance measures govern different tasks. The underlying problem is characterized by the calculation (or prediction) of the human performance measure in such a way as to accomplish a specified task. In this work, we have not limited the number of degrees of freedom associated with the model. Each task has been defined by a number of human performance measures that are mathematically represented by cost functions that evaluate to a real number. Cost functions are then optimized, i.e., minimized or maximized, subject to a number of constraints, including joint limits. The formulation is demonstrated and validated. We present this computational formulation as a broadly applicable algorithm for predicting postures using one or more human performance measures. © 2008 Cambridge University Press.