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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.

The Snook tables (Liberty Mutual Tables) are a collection of data sets compiled from studies based on a psychophysical approach to material-handling tasks. These tables are used to determine safe loads for lifting, lowering, carrying pulling, and pushing. The tables take into account different population percentiles, gender, and frequency of activity. However, while using these tables to analyze a work place, Ergonomists often have to select from discrete data points closest to the actual work place parameters thereby reducing accuracy of results. To compound the problem further, multiple interrelated variables are involved, making it difficult to analyze parameters intuitively. For example, it can be difficult to answer questions such as, does reducing the lifting height lower the recommended lifting weight, if the lifting distance is increased? To resolve such issues, this paper presents a new methodology for implementing the Snook tables using multi variable regression.

In this study, an optimization-based approach for simulating the lifting motion of a three dimensional digital human model is presented. Lifting motion is generated by minimizing a performance measure subjected to basic physical and kinematical constraints. Two performance measures are investigated: one is the dynamic effort; the other is the compression and shear forces on the lumbar joint. The lifting strategies are predicted with different performance measures. The joint strength (torque limit) and the compression and shear force on lumbar joint are also addressed in this study to avoid injury during lifting motion.

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.

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.

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.

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.

In this study, an optimization-based approach for simulating the walking motion of a digital human model is presented. A spatial skeletal model having 55 degrees of freedom is used to demonstrate the approach. Design variables are the joint angle profiles. Walking motion is generated by minimizing the mechanical energy subjected to basic physical and kinematical constraints. A formulation for symmetric and periodic normal walking is developed and results are presented. Backpack and ground reaction forces are taken into account in the current formulation, and the effects of the backpack on normal walking are discussed.

This paper presents new capabilities of the virtual-human Santos™ introduced last year. Santos™ is an avatar that has extensive modeling and simulation features. It is a digital human model with over 100 degrees-of-freedom (DOF), where the hand model has 25 DOF, direct optimization-based method, and real-human like appearance. The newly developed analysis includes (1) a 25-DOF hand model that is the first step to study hand grasping; (2) posture prediction advances such as multiple end-effectors (two arms, two arms + head + legs), real-time inverse kinematics for posture prediction for any points, vision functionality; (3) dynamic motion prediction with external loads; and (4) musculosteletal modeling that includes determining muscle forces, and muscle stress.

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.

The effect of restrictive clothing on functional reach and on balance and gait during obstacle crossing of five normal subjects is presented in this work using motion capture and stability analyses. The study has shown that restrictive clothing has considerably reduced participants' functional reach. It also forced the participants to change their motion strategy when they cross-higher obstacles. When crossing higher obstacles, the participants averted their stance foot, abducted their arms, flexed their torso, used longer stance time, and increased their hip angle in the medial-lateral (Rolling) and vertical (Yawing) directions. The stability analysis of a virtual human skeletal model with 18 links and 25 degrees of freedom has shown that participants' stability has become critical when they wear restrictive clothing and when they cross higher obstacles.

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.

Simulating human motion is a complex problem due to redundancy of the human musculoskeletal system. The concept of task-based motion prediction using single- or multi-objective optimization techniques provides a viable approach for predicting intermediate motions of digital humans. It is shown that task-based motion prediction is in fact a numerical optimal control problem. Alternative formulations for simulation of human motion are possible and can be solved by modern nonlinear optimization methods. Three techniques based on state variable elimination, direct collocation and differential inclusion are presented and compared. The basic idea of the formulations is to treat different combinations of the state variables, such as the joint profiles and torques or their parametric representations as independent variables in the optimization process.

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.

Experimental analysis of human motion has been based on optical, magnetic, or electronic tracking techniques to determine body segment locations and orientations. The Average Coordinate Reference System (ACRS) method was developed to reduce experimental errors in human locomotion analysis. Experimentally measured kinematic data is used to conduct analysis in human modeling, and the model accuracy is directly related to the accuracy of the data. However, the accuracy is questionable due to skin movement, deformation of skeletal structure while in motion and limitations of commercial motion analysis systems. In this study, the ACRS method is applied to an optically-tracked segment marker system, although it can be applied to many of the others as well. Many previous studies adopted redundant marker systems, using four or five optical markers, instead of the basic three marker system to provide statistically better results of body segment position and orientation.

A summary is given of the enabling technologies for real time high fidelity vehicle dynamics simulation. Methods of utilizing this technology to increase realism in an operator in the loop simulation are then discussed. Finally some of the research that can be performed using a high fidelity, highly realistic operator in the loop simulator is presented. Automotive engineers have long used sophisticated, batch job computer simulations of the dynamics of vehicles and vehicle subsystems to aid them in improving vehicle performance and safety. Recent technological advances have brought high-fidelity vehicle dynamics simulation into a new realm; that of real time.