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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.

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.

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.

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 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.

Significant attention in recent years has been given towards obtaining a better understanding of human joint ranges, measurement, and functionality, especially in conjunction with commands issued by the central nervous system. Studies of those commands often include computer algorithms to describe path trajectories. These are typically in “open-form” with specific descriptions of motions, but not “closed form” mathematical solutions of the full range of possibilities. This paper proposes a rigorous “closed form” kinematic formulation to model human limbs, understand their workspace (also called the reach envelope), and delineate barriers therein where a path becomes difficult or impossible owing to physical constraints. The novel ability to visualize barriers in the workspace emphasizes the power of these closed form equations.

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 hands are the bridge between humans and the objects to be manipulated or grasped both in the real and virtual world. Hands are used to grasp or manipulate objects and one of the most important functionalities is to position the fingers, i.e., given the position of the fingertip and to determine the joint angles. Last year we presented a 25-degree of freedom (DOF) hand model that has palm arch functionality. In this paper we preset an optimization-based inverse kinematics approach to position this 25 DOF hand locally with respect to the wrist instead of the traditional Moore-Penrose pseudo-inverse and experiment methods. The hypothesis is that human performance measures govern the configuration and motion of the hand. We also propose contact force and joint torque prediction.

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.

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.

Design and packaging of automotive interiors and airplane cockpits has become a science in itself, particularly in recent years where safety is paramount. There are various methods for restraining operators in their seats, including fitting an operator, such as a race car driver or pilot, with two seat belts, one for each side of the body, a three point restraining system as in commercial vehicles, and a lap belt as in some trucks and other types of vehicles. Moreover, significant experimental efforts have been made to study driver reach and barriers since they directly affect performance and safety. This paper presents a rigorous formulation for addressing the reach envelope and barriers therein of a 3-point restrained driver compared with a lap-belt-restrained driver. The formulation is based on a kinematic model of the driver, which characterizes the upper body and arm as 7 degrees of freedom (DOF) for an unrestrained and 4DOF for a 3-point restrained driver.

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.

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.