Computational Modeling: Human Dynamic Model

Abstract

Improvements in quantitative measurements of human physical activity are proving extraordinarily useful for studying the underlying musculoskeletal system. Dynamic models of human movement support clinical efforts to analyze, rehabilitate injuries. They are also used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. In addition, they provide useful constraints for underlying neural circuits. This paper describes a physics-based movement analysis method for analyzing and simulating bipedal humanoid movements. A 48 degree of freedom dynamic model of humans has been developed to report humanoid movements energetic components. It has sufficient speed and accuracy to analyze and synthesize real-time interactive applications, such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to believably animate a humanoid character or estimate internal joint forces used during a movement for creating effort-contingent experimental stimuli. A virtual reality environment developed as part of this research supports controlled experiments for systematically recording human behaviors.