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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.

Collision avoidance in digital human modeling is critical for design and analysis, especially when there is interaction between the avatar and his/her environment. This paper describes a new algorithm for obstacle avoidance with optimization-based posture prediction. This new approach is motivated by a need for decreased computational time and increased fidelity for modeling and analysis of collision avoidance tasks. Posture prediction is run in an iterative loop while conducting collision detection to dynamically update collision avoidance constraints. It is shown that this approach is substantially faster than the basic method involving a fixed number of sphere-based avoidance constraints with a single optimization/posture-prediction run. The method is demonstrated using an upper-body virtual human model in a cab setting.

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.

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.

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.

This paper presents a novel approach to determining the joint motion coupling relationship for the human shoulder complex. The human shoulder complex is the most sophisticated part in terms of degrees of freedom and motion. In the literature, different human shoulder biomechanical models have been developed for various purposes. Also, researchers have realized that there are constant movement relationships among the shoulder bones: the clavicle, scapula, and humerus. This is due to muscles and tendons that are involved in skeletal motions. These relationships, which are also called shoulder rhythm, entail joint motion coupling and joint limit coupling. However, the scope of this work is to determine the joint motion coupling relationship. This relationship is available in the literature, but it is an Euler-angle-based relationship. In the virtual human modeling environment, we cannot directly use this Euler-angle-based relationship.

The human shoulder plays an important role in human posture and motion, especially in scenarios in which humans need achieve tasks with external loads. The shoulder complex model is critical in digital human modeling and simulation because a fidelity model is the basis for realistic posture and motion predictions for digital humans. The complexity of the shoulder mechanism makes it difficult to model a shoulder complex realistically. Although many researchers have attempted to model the human shoulder complex, there has not been a survey of these models and their benefits and limitations. This paper attempts to review various biomechanical models proposed and summarize the pros and cons. It focuses mainly on the human modeling domain, although some of these models were originally from the robotics field. The models are divided into two major categories: open-loop chain models and closed-loop chain models.

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.

Over the past several years, significant advances have been made in the area of posture prediction. However, to make simulations more useful for vehicle design, additional unique tools are needed. This research focuses on the development of one such tool, called zone differentiation. This new tool allows user to visualize not only the complete reach envelope but also the interior comfort levels of the envelope. It uses a color map to display the relative values of various performance measures (i.e. comfort) at points surrounding an avatar. This is done by leveraging an optimization-based approach to posture prediction. Using this tool, a vehicle designer can visually display the impact that the placement of a control (switch, button, etc.) has on a driver's postural comfort. The comfort values are displayed in a manner similar to how a finite element analysis (FEA) programs display stress and strain results. The development of this tool requires two main components.

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.

With the ever-increasing power of real time graphics and computational ability of desktop computers, the desire for a real-time simulation of the musculoskeletal system has become more pronounced. It is important that this simulation is realistic, interactive, runs in real time, and looks realistic, especially in our climate of Hollywood special-effects and stunning video games. An effective simulation of the musculoskeletal system hinges on three key features: accurate modeling of kinematic movement, realistic modeling of the muscle attachment points, and determining the direction of the forces applied at the points. By taking known information about the musculoskeletal system and applying it in a real time environment, we have created such a model of the human arm. This model includes realistic constraints on the joints and real-time wrapping algorithms for muscle action lines.

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.

A framework to study the response of seated operators to whole-body vibration (WBV) is presented in this work. The framework consists of (i) a six-degree-of-freedom man-rated motion platform to play back ride files of typical heavy off-road machines; (ii) an optical motion capture system to collect 3D motion data of the operators and the surrounding environment (seat and platform); (iii) a computer skeletal model to embody the tested subjects in terms of their body dimensions, joint centers, and inertia properties; (iv) a marker placement protocol for seated positions that facilitates the process of collecting data of the lower thoracic and the lumbar regions of the spine regardless of the existence of the seatback; and (v) a computer human model to solve the inverse kinematics/dynamic problem for the joint profiles and joint torques. The proposed framework uses experimental data to answer critical questions regarding human response to WBV.

This paper presents newly developed capabilities for the virtual human Santos™. Santos is an avatar that has extensive modeling and simulation features. It is a digital human with 109 degrees of freedom (DOF), an optimization-based method, predictive dynamics, and realistic human appearance. The new capabilities include (1) significant progress in predictive dynamics (walking and running), (2) advanced clothing modeling and simulation, (3) muscle wrapping and sliding, and (4) hand biomechanics. With these newly developed functionalities, Santos can simulate various dynamic tasks such as walking and running, investigate clothing restrictions to motion such as joint limits and torques, simulate the musculoskeletal system in real time, predict hand injury by monitoring the joint torques, and facilitate vehicle interior design. Finally, additional on-going projects are summarized.

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.

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.

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.