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In the field of human modeling, there is an increasing demand for predicting human postures in real time. However, there has been minimal progress with methods that can incorporate multiple limbs with shared degrees of freedom (DOFs). This paper presents an optimization-based approach for predicting postures that involve dual-arm coordination with shared DOFs, and applies this method to a 30-DOF human model. Comparisons to motion capture data provide experimental validation for these examples. We show that this optimization-based approach allows dual-arm coordination with minimal computational cost. This new approach also easily extends to models with a higher number of DOFs and additional end-effectors.

Using multi-objective optimization, we develop a new human performance measure for direct optimizationbased posture prediction that incorporates three key factors associated with musculoskeletal discomfort: 1) the tendency to move different segments of the body sequentially, 2) the tendency to gravitate to a comfortable neutral position, and 3) the discomfort associated with moving while joints are near their respective limits. This performance measure operates in real-time and provides realistic postures. The results are viewed using Santos™, an advanced virtual human, and they are validated using motion-capture. This research lays groundwork for studying how and why humans move as they do.

This paper presents an optimization-based algorithm for simulating the dynamic motion of a digital human. We also formulate the metabolic energy expenditure during the motion, which is calculated within our algorithm. This algorithm is implemented and applied to Santos™, an avatar developed at The University of Iowa. Santos™ is a part of a virtual environment for conducting digital human analysis consisting of posture prediction, motion prediction, and physiology studies. This paper demonstrates our dynamic motion algorithm within the Santos™ virtual environment. Mathematical evaluations of human performance are essential to any effort to compare various ergonomic designs. In fact, the human factors design process can be formulated as an optimization problem that maximizes human performance. In particular, an optimal design must be found while taking into consideration the effects of different motions and hand loads corresponding to a number of tasks.

Workspace is an important function for human factors analysis and is widely applied in product design, manufacturing, and ergonomics evaluations. This paper presents the workspace analysis and visualization for Santos™ upper extremity, a new virtual human with over 100 DOFs that is highly realistic in terms of appearance, behavior, and movement. Jacobian Rank deficiency method is implemented to determine the singular surfaces. The joint limits are considered in this formulation; three types of singularities are analyzed. This closed-form formulation can be extended to numerous different scenarios such as different percentiles, age groups, or segments of body. A realtime scheme is used to build the workspace library for Santos™ that will study the boundary surfaces off-line and apply them to Santos™ in the virtual environment (Virtools®). To visualize the workspace, we develop a user interface to generate the cross section of the reach envelope with a plane.

This paper presents a SANTOS™ 25 degree-of-freedom (DOF) hand model and the forward and inverse kinematic analysis. The Denavit-Hartenberg (D-H) method is used to define the position of the end- effector (fingertip). In the SANTOS™ hand model each finger has different constraints and movements (e.g., the middle finger in distal Interphalangeal (DIP) joint can move in Flexion/Extension (F/E) with a range 0–100 degrees, and the thumb in interphalangeal (IP) joint can rotate in F/E with arrange of 15H/80). Including hand model SANTOS™ has over 100 DOFs and the forward and inverse kinematics have been studied. Optimization-based dynamic motion prediction will be used to consider different gestures for hand grasping.