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Human performance measures such as discomfort and joint displacement play an important role in product design. The virtual human Santos™, a new generation of virtual humans developed at the University of Iowa, goes directly to the CAD model to evaluate a design, saving time and money. This paper presents an optimization-based workspace zone differentiation and visualization. Around the workspace of virtual humans, a volume is discretized to small zones and the posture prediction on each central point of the zone will determine whether the points are outside the workspace as well as the values of different objective functions. Visualization of zone differentiation is accomplished by showing different colors based on values of human performance measures on points that are located inside the workspace. The proposed method can subsequently help ergonomic design.

Presented in this paper is an on-going project to develop a new generation of virtual human models that are highly realistic in terms of appearance, movement, and feedback (evaluation of the human body during task execution). Santos™ is an avatar that exhibits extensive modeling and simulation capabilities. It is an anatomically correct human model with more than 100 degrees of freedom. Santos™ resides in a virtual environment and can conduct human-factors analysis. This analysis entails, among other things, posture prediction, motion prediction, gait analysis, reach envelope analysis, and ergonomics studies. There are essentially three stages to developing virtual humans: (1) basic human modeling (representing how a human functions independently), (2) input functionality (awareness and analysis of the human’s environment), and (3) intelligent reaction to input (memory, reasoning, etc.). This paper addresses the first stage.

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 an algorithm of predicting dynamic biped motions of Santos™ human model. An alternative and efficient formulation of the Zero-Moment Point (ZMP) for dynamic balance and the approximated ground reaction forces/moments are derived from the resultant reaction loads, which includes the gravity, the externally applied loads, and the inertia. The optimization problem is formulated to address the redundancy of the human task, where the general biped and the task-specific constraints are imposed depending on the task requirements. The proposed method is fully predictive and generates physically feasible human-like motions from scratch without any input reference from motion capture or animation. The resulting generated motions demonstrate how a human reacts effectively to different external load conditions in performing a given task by showing realistic features of cause and effect.