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This paper describes an effective integrated method for estimation of subject-specific mass, inertia tensor, and center of mass of individual body segments of a digital avatar for use with physics-based digital human modeling simulation environment. One of the main goals of digital human modeling and simulation environments is that a user should be able to change the avatar (from male to female to a child) at any given time. The user should also be able to change the various link dimensions, like lengths of upper and lower arms, lengths of upper and lower legs, etc. These customizations in digital avatar's geometry change the kinematic and dynamic properties of various segments of its body. Hence, the mass and center of mass/inertia data of the segments must be updated before simulating physics-based realistic motions. Most of the current methods use mass and inertia properties calculated from a set of regression equations based on average of some population.

A framework to validate the predicted motion of a computer human model (Santos) is presented in this work. The proposed validation framework is a task-based methodology. It depends on the comparison of selected motion determinants and joint angles that play major roles in the task, using qualitative and quantitative statistical techniques. In the present work, the validation of Santos walking will be presented. Fortunately, the determinants for normal walking are well defined in the literature and can be represented by (i) hip flexion/extension, (ii) knee flexion/extension, (iii) ankle plantar/dorsiflexion, (iv) pelvic tilt, (v) pelvic rotation, and (vi) lateral pelvic displacement. While Santos is an ongoing research project, the results have shown significant qualitative agreements between the walking determinants of Santos and the walking determinants of four normal subjects.

Our previous formulation for optimization-based dynamic motion simulation of a serial-link human upper body (from waist to right hand) is extended to predict the motion of a tree-structured human model that includes the torso, right arm, and left arm, with various applied external loads. The dynamics of tree-structured systems is formulated and implemented. The equations of motion for the tree structures must be derived carefully when dealing with the connection link. The optimum solution results show realistic dual-arm human motions and the required joint actuator torques. In the second part of this paper, a new method is introduced in which the constraint forces and moments at the joints are calculated along with the motion and muscle-induced actuator torques. A set of fictitious joints are modeled in addition to the real joints.

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.

A general methodology and associated computational algorithm for predicting realistic postures of digital humans (mannequins) in a virtual environment is presented. The basic plot for this effort is a task-based approach, where we believe that humans assume different postures for different tasks. The underlying problem is characterized by the calculation (or prediction) of the joint displacements of the human body in such a way 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. The problem is formulated as a multi-objective optimization algorithm where one or more cost functions are considered as objective functions that drive the model to a solution.