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Simulating human motion is a complex problem due to redundancy of the human musculoskeletal system. The concept of task-based motion prediction using single- or multi-objective optimization techniques provides a viable approach for predicting intermediate motions of digital humans. It is shown that task-based motion prediction is in fact a numerical optimal control problem. Alternative formulations for simulation of human motion are possible and can be solved by modern nonlinear optimization methods. Three techniques based on state variable elimination, direct collocation and differential inclusion are presented and compared. The basic idea of the formulations is to treat different combinations of the state variables, such as the joint profiles and torques or their parametric representations as independent variables in the optimization process.

This paper presents new capabilities of the virtual-human Santos™ introduced last year. Santos™ is an avatar that has extensive modeling and simulation features. It is a digital human model with over 100 degrees-of-freedom (DOF), where the hand model has 25 DOF, direct optimization-based method, and real-human like appearance. The newly developed analysis includes (1) a 25-DOF hand model that is the first step to study hand grasping; (2) posture prediction advances such as multiple end-effectors (two arms, two arms + head + legs), real-time inverse kinematics for posture prediction for any points, vision functionality; (3) dynamic motion prediction with external loads; and (4) musculosteletal modeling that includes determining muscle forces, and muscle stress.