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Industrial tasks performed by standing workers are among those most commonly simulated using digital human models. Workers often walk, turn, and take acyclic steps as they perform these tasks. Current h uman modeling tools lack the capability to simulate these whole body motions accurately. Most models simulate walking by replaying joint angle trajectories corresponding to a general gait pattern. Turning is simulated poorly if at all, and violations of kinematic constraints between the feet and ground are common. Moreover, current models do not accurately predict foot placement with respect to loads and other hand targets, diminishing the utility of the associated ergonomic analyses. A new approach to simulating stepping and walking in task-oriented activities is proposed. Foot placements and motions are predicted from operator and task characteristics using empirical models derived from laboratory data and validated using field data from an auto assembly plant.

Human figure models are commonly used to facilitate ergonomic assessments of vehicle driver stations and other workplaces. One routine method of workstation assessment is to conduct a suite of ergonomic analyses using a family of boundary manikins, chosen to represent a range of anthropometric extremes on several dimensions. The suitability of the resulting analysis depends both on the methods by which the boundary manikins are selected and on the methods used to posture the manikins. The automobile driver station design problem is used to examine the relative importance of anthropometric and postural variability in ergonomic assessments. Postural variability is demonstrated to be nearly as important as anthropometric variability when the operator is allowed a substantial range of component adjustment. The consequences for boundary manikin procedures are discussed, as well as methods for conducting accurate and complete assessments using the available tools.

Most current applications of digital human figure models for ergonomic assessments of manual tasks focus on the analysis of a static posture. Tools available for static analysis include joint-specific strength, calculation of joint moments, balance maintenance capability, and low-back compression or shear force estimates. Yet, for many tasks, the inertial loads due to acceleration of body segments or external objects may contribute significantly to internal body forces and tissue stresses. Due to the complexity of incorporating the dynamics of motion into analysis, most commercial software packages used for ergonomic assessment do not have the capacity to include dynamic effects. Thus, commercial human modeling packages rarely provide an opportunity for the user to determine if a static analysis is sufficient.

A major limitation of ergonomic analyses with current digital human models (DHM) is the speed and accuracy with which they can simulate worker postures and motions. Ergonomic analysis capabilities of DHM would be significantly improved with the addition of a fast, deterministic, accurate movement simulation model for the upper extremities. This paper describes the development of an important component of such a model. Motion data from twelve men and women performing one-handed, push-button reaches in a heavy truck seat were analyzed to determine patterns of motion of the clavicle relative to the thorax. Target direction and reach distance were good predictors clavicle segment motion, particularly for fore-aft clavlcle motion.

Most human figure models used in ergonomic analyses present postural comfort ratings based on joint angles, and present a single comfort score for the whole body or on a joint-by-joint basis. The source data for these ratings is generally derived from laboratory studies that link posture to ratings. Lacking in many of these models is a thorough treatment of the distribution of ratings for the population of users. Information about ratings distributions is necessary to make cost-effective tradeoffs when design changes affect subjective responses. This paper presents experimental and analytic methods used to develop distribution models for incorporating subjective rating data in ergonomic assessments.

Posture and external loads such as hand forces have a dominant effect on ergonomic analysis outcomes. Yet, current digital human modeling tools used for proactive ergonomics analysis lack validated models for predicting postures for standing hand-force exertions. To address this need, the effects of hand magnitude and direction on whole-body posture for standing static hand-force exertion tasks were quantified in a motion-capture study of 19 men and women with widely varying body size. The objective of this work was to identify postural behaviors that might be incorporated into a posture-prediction algorithm for standing hand-force tasks. Analysis of one-handed exertions indicates that, when possible, people tend to align their bodies with the direction of force application, converting potential cross-body exertions into sagittal plane exertions. With respect to the hand-force plane, pelvis position is consistent with a postural objective of reducing rotational trunk torques.

For many industrial tasks (push, pull, lift, carry, etc.), restrictions on grip locations and visibility constrain the hand and head positions and help to define feasible postures. In contrast, foot locations are often minimally constrained and an ergonomics analyst can choose several different stances in selecting a posture to analyze. Also, because stance can be a critical determinant of a biomechanical assessment of the work posture, the lack of a valid method for placing the feet of a manikin with respect to the task compromises the accuracy of the analysis. To address this issue, foot locations and orientations were captured in a laboratory study of sagittal plane and asymmetric manual load transfers. A pilot study with four volunteers of varying anthropometry approached a load located on one of three shelves and transferred the load to one of six shelves.

Accurate representation of working postures is critical for ergonomic assessments with digital human models because posture has a dominant effect on analysis outcomes. Most current digital human modeling tools require manual manipulation of the digital human to simulate force-exertion postures or rely on optimization procedures that have not been validated. Automated posture prediction based on human data would improve the accuracy and repeatability of analyses. The effects of hand force location, magnitude, and direction on whole-body posture for standing tasks were quantified in a motion-capture study of 20 men and women with widely varying body size. A statistical analysis demonstrated that postural variables critical for the assessment of body loads can be predicted from the characteristics of the worker and task.

Workstation design is one of the most essential components of proactive ergonomics, and digital human models have gained increasing popularity in the analysis and design of current and future workstations (Chaffin 2001). Using digital human technology, it is possible to simulate interactions between humans and current or planned workstations, and conduct quantitative ergonomic analyses based on realistic human postures and motions. Motion capture has served as the primary means by which to acquire and visualize human motions in a digital environment. However, motion capture only provides motions for a specific person performing specific tasks. Albeit useful, at best this allows for the analysis of current or mocked-up workstations only. The ability to subsequently modify these motions is required to efficiently evaluate alternative design possibilities and thus improve design layouts.

The potential of digital human modeling to improve the design of products and workspaces has been limited by the time-consuming manual manipulation of figures that is required to perform simulations. Moreover, the inaccuracies in posture and motion that result from manual procedures compromise the fidelity of the resulting analyses. This paper presents a new approach to the control of human figure models and the analysis of simulated tasks. The new methods are embodied in an algorithmic framework developed in the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. The framework consists of an interconnected, hierarchical set of posture and motion modules that control aspects of human behavior, such as gaze or upper-extremity motion. Analysis modules, addressing issues such as shoulder stress and balance, are integrated into the framework.

Digital human modeling has traditionally focused on the physical aspects of humans and the environments in which they operate. As the field moves towards modeling dynamic and more complex tasks, cognitive and perceptual aspects of the human's performance need to be considered. Cognitive modeling of complex tasks such as driving has commonly avoided the complexity of physical simulation of the human, distilling motor performance to motion execution times. To create a more powerful and flexible approach to the modeling of human/machine interaction, we have integrated a physical architecture of human motion (the Human Motion Simulation Ergonomics Framework—HUMOSIM) with a computational cognitive architecture (the Queueing network model human processor—QN–MHP). The new system combines the features of the two separate architectures and provides new capabilities that emerge from their integration.

The Human Motion Simulation Framework is a hierarchical set of algorithms for physical task simulation and analysis. The Framework is capable of simulating a wide range of tasks, including standing and seated reaches, walking and carrying objects, and vehicle ingress and egress. In this paper, model predictions for the terminal postures of standing object transfer tasks are compared to data from 20 subjects with a wide range of body dimensions. Whole body postures were recorded using optical motion capture for one-handed and two-handed object transfers to target destinations at three angles from straight ahead and three heights. The hand and foot locations from the data were input to the HUMOSIM Framework Reference Implementation (HFRI) in the Jack human modeling software. The whole-body postures predicted by the HFRI were compared to the measured postures using a set of measures selected for their importance to ergonomic analysis.