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A method to simulate digital human running using an optimization-based approach is presented. The digital human is considered as a mechanical system that includes link lengths, mass moments of inertia, joint torques, and external forces. The problem is formulated as an optimization problem to determine the joint angle profiles. The kinematics analysis of the model is carried out using the Denavit-Hartenberg method. The B-spline approximation is used for discretization of the joint angle profiles, and the recursive formulation is used for the dynamic equilibrium analysis. The equations of motion thus obtained are treated as equality constraints in the optimization process. With this formulation, a method for the integration of constrained equations of motion is not required. This is a unique feature of the present formulation and has advantages for the numerical solution process.

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.

As predictive capabilities advance and human-model fidelity increases, so must validation of such predictions and models. However, subjective validation is sufficient only as an initial indicator; thorough, systematic studies must be conducted as well. Thus, the purpose of this paper is to validate postures that are determined using single-objective optimization (SOO) and multi-objective optimization (MOO), as applied to the virtual human Santos™. In addition, a general methodology and tools for posture-prediction validation are presented. We find that using MOO provides improvement over SOO, and the results are realistic from both a subjective and objective perspective.

The objective of this paper is to present our method of predicting and simulating visually realistic and dynamically consistent human stair-climbing motion. The digital human is modeled as a 55-degrees of freedom branched mechanical system with associated human anthropometry-based link lengths, mass moments of inertia, and centers of gravity. The joint angle profiles are determined using a B-spline-based parametric optimization technique subject to different physics-based, task-based, and environment-based constraints. The formulation offers the ability to study effects of the magnitude and location of external forces on the resulting joint angle profiles and joint torque profiles. Several virtual experiments are conducted using this optimization-based approach and results are presented.

Inverse Kinematics on a human model combined with optimization provides a powerful tool to predict realistic human postures. A human posture prediction tool brings up the need for greater flexibility for the user, as well as efficient computation performance. This paper demonstrates new methods that were developed for the application of digital human simulation as a software package by allowing for any number of user specified end-effectors and increasing communication efficiency for posture prediction. The posture prediction package for the digital human, Santos™, uses optimization constrained by end-effectors on the body with targets in the environment, along with variable cost functions that are minimized, to solve for all joint angles in a human body. This results in realistic human postures which can be used to create optimal designs for things that humans can physically interact with.

This paper describes a novel model-based controller designed to simulate human motion in dynamic virtual environments. The controller was tested on SantosTM, the digital human developed at the Virtual Soldier Research Program at the University of Iowa. A planar 3-degrees-of-freedom model of the human arm was used to test the hypothesis. The controller was used to predict on line, optimal torques required to move the end effector towards a target point. The control law was implemented using classical gradient-based optimization and the recently developed technique of model predictive control (MPC). An advantage of MPC is that it replaces intractable closed loop optimization problems with more easily implementable open loop problems. The controller was used to produce physically consistent simulations of the motion of a human arm in a virtual environment in the presence of external disturbances that were not known in advance.

This paper presents an efficient numerical formulation for the prediction of realistic postures. This problem is defined by the method (or procedure) used to predict the posture of a human, given a point in the reachable space. The exposition addresses (1) the determination whether a point is reachable (i.e., does is it exist within the reach envelope) and (2) the calculation of a posture for a given point. While many researchers have used either statistical models of empirical data or the traditional geometric inverse kinematics method for posture prediction, we present a method based on kinematics for modeling, but one that uses optimization of a cost function to predict a realistic posture. It is shown that this method replicates the human mind in selecting a posture from an infinite number of possible postures.

Although much work has been completed with modeling head-neck movements as well with studying the intricacies of vision and eye movements, relatively little research has been conducted involving how vision affects human upper-body posture. By leveraging direct human optimized posture prediction (D-HOPP), we are able to predict postures that incorporate one's tendency to actually look towards a workspace or see a target. D-HOPP is an optimization-based approach that functions in real time with Santos™, a new kind of virtual human with a high number of degrees-of-freedom and a highly realistic appearance. With this approach, human performance measures provide objective functions in an optimization problem that is solved just once for a given posture or task. We have developed two new performance measures: visual acuity and visual displacement.

An optimization-based method for layout design (also called equipment layout) is presented that is based upon kinetic functions also introduced in this paper. The layout problem is defined by the method whereby positions of target points are specified in the environment surrounding a human. The problem is of importance to ergonomists, vehicle/cockpit packaging engineers, designers of manufacturing assembly lines, and designers concerned with the placement of lever, knobs, and controls in the reachable workspace of a human, but also to users of digital human modeling code, where digital prototyping has become a valuable tool. The method comprises kinematically-driven constraints for reaching the target points and for satisfying the joint ranges of motion. The algorithm is driven by a cost function (also called objective function) that is kinetic in nature to minimize approximate energy consumption and visual discomfort.

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.

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.

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.

Using optimization to predict human posture provides a unique means of studying how and why people move. In a formulation where joint angles are determined in order to minimize a human performance measure subject to various constraints, the general question of when to model components as objective functions and when to model them as constraints has not been addressed thoroughly. We suggest that human performance measures, which act as objective functions, model what drives human posture, whereas constraints provide boundary conditions that restrict the scope of the model. This applied research study tests this hypothesis and concurrently evaluates how vision affects the prediction and assessment of upper-body posture. Single-objective and multi-objective optimization formulations for posture prediction are used with a 35 degree-of-freedom upper-body model of a virtual human called SantosTM.

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.