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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.

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.

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 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.

A robust design procedure is applied to achieve improved vehicle handling performance as an integral part of simulation-based vehicle design. This paper presents a hybrid robust design method, the robust design process strategy (RDPS), which makes full use of the intense complementary action of characteristics between the Response Surface Methodology (RSM) and the Taguchi method, to get the robust design of the vehicle handling performance. The vehicle multi-body dynamic model is built in the platform that is constructed by the software of iSIGHT, ADAMS/CAR, and MATLAB. The design-of-experiment method of the Latin Hypercube (LHC) is used to obtain the approximate area values, and then the RDPS is utilized to achieve improved vehicle handling performance results. The validation is made by the Monte Carlo Simulation Technique (MCST) in terms of the effectiveness of the RDPS in solving robust design problems.

Presented in this paper is an on-going project to develop a new generation of virtual human models that are highly realistic in terms of appearance, movement, and feedback (evaluation of the human body during task execution). Santos™ is an avatar that exhibits extensive modeling and simulation capabilities. It is an anatomically correct human model with more than 100 degrees of freedom. Santos™ resides in a virtual environment and can conduct human-factors analysis. This analysis entails, among other things, posture prediction, motion prediction, gait analysis, reach envelope analysis, and ergonomics studies. There are essentially three stages to developing virtual humans: (1) basic human modeling (representing how a human functions independently), (2) input functionality (awareness and analysis of the human’s environment), and (3) intelligent reaction to input (memory, reasoning, etc.). This paper addresses the first stage.

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.

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.

This paper investigates a hierarchical optimization procedure for the optimum synthesis of a double-axle steering mechanism by considering the dynamic load of a vehicle which is seldom discussed in the previous literature. Firstly, a multi-body model of double-axle steering is presented by characterizing the detailed leaf spring effect. Accordingly, the influences of dynamic load including the motion interference of steering linkage resulted from the elastic deformation of leaf spring, and the effects of wheel slip angle and the position discrepancy of wheel speed rotation centers are explored systematically. And then, a hierarchical optimization method based on target cascading methodology is proposed to classify the design variables of double-axle steering mechanism into four levels. At last, a double-axle steering mechanism of a heavy-duty truck is utilized to demonstrate the validity of this method.

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.

There are many sources of air pollution that can threaten air quality during space missions. The International Space Station (ISS) is an extremely complex platform that depends on a multi-tiered strategy to control the risk of excessive air pollution. During the seven missions surveyed by this report, the ISS atmosphere was in a safe, steady-state condition; however, there were minor loads added as new modules were attached. There was a series of leaks of octafluoropropane, which is not directly toxic to humans, but did cause changes in air purification operations that disrupted the steady state condition. In addition, off-nominal regeneration of metal oxide canisters used during extravehicular activity caused a serious pollution incident.

Space-faring crews must have safe breathing air throughout their missions to ensure adequate performance and good health. Toxicological assessment of air quality depends on the standards that define acceptable air quality, measurements of pollutant levels during the flight, and reports from the crew on their in-flight perceptions of air quality. Air samples returned from ISS on flights 8A, UF2, 9A, and 11A were analyzed for trace pollutants. On average, the air during this period of operations was safe for human respiration. However, about 3 hours into the regeneration of 2 Metox canisters in the U.S. airlock on 20 February 2002 the crew reported an intolerable odor that caused them to stop the regeneration, take refuge in the Russian segment, and scrub air in the U.S. segment for 30 hours. Analytical data from grab samples taken during the incident showed that the pollutants released were characteristic of nominal air pollutants, but were present in much higher concentrations.

Space-faring crews must have safe breathing air throughout their missions to ensure adequate performance and good health. Toxicological assessment of air quality depends on the standards that define acceptable air quality, measurements of pollutant levels during the flight, and reports from the crew on their in-flight perceptions of air quality. Air samples from ISS flights 2A.2a, 2A.2b, 3A, and 4A were analyzed for trace pollutants. On average the air during each flight was safe for human respiration. However, there were reports from the crew that they experienced a headache when in certain areas, and strong odors were reported from specific locations of the ISS complex. Inspection of air samples in these locations suggested that several of the solvent-type pollutants (e.g. ethyl acetate, xylenes, and n-butanol) were present in concentrations that would cause a strong odor to be perceived by some individuals.

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 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.

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.

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.