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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.

Modeling normal human reach behavior is dependent on many factors. Anthropometry, age, gender, joint mobility and muscle strength are a few such factors related to the individual being modeled. Reach locations, seat configurations, and tool weights are a few other task factors that can affect dynamic reach postures. This paper describes how two different modeling approaches are being used in the University of Michigan Human Motion Simulation Laboratory to predict normal seated reaching motions. One type of model uses an inverse kinematic structure with an optimization procedure that minimizes the weighted sum of the instantaneous velocity of each body segment. The second model employs a new functional regression technique to fit polynomial equations to the angular displacements of each body segment. To develop and validate these models, 38 subjects of widely varying age and anthropometry were asked to perform reaching motions while seated in simulated vehicle or industrial workplace.

Simulation of human motions in virtual environments is an essential component of human CAD (Computer-aided Design) systems. In our earlier SAE papers, we introduced a novel motion simulation approach termed Memory-based Motion Simulation (MBMS). MBMS utilizes existing motion databases and predicts novel motions by modifying existing ‘root’ motions through the use of the motion modification algorithm. MBMS overcomes some limitations of existing motion simulation models, as 1) it simulates different types of motions on a single, unified framework, 2) it simulates motions based on alternative movement techniques, and 3) like real humans, it can learn new movement skills continually over time. The current study evaluates the prediction accuracy of MBMS to prove its utility as a predictive tool for computer-aided ergonomics. A total of 627 whole-body one-handed load transfer motions predicted by the algorithm are compared with actual human motions obtained in a motion capture experiment.

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.

This paper describes and illustrates two different approaches used to predict population strength capabilities. One approach uses statistical regression models to predict maximum acceptable weight or force capabilities as a function of population attributes (e.g., anthropometry, gender and standardized strength scores). A second approach relies on a biomechanical concept stating that the maximum force one can produce in a given posture and for a given type of exertion is a function of the minimum relative strength produced by one's muscles at a particular joint. This latter approach has resulted in a specific static strength prediction model which has been implemented in software produced by the Center for Ergonomics at the University of Michigan. The software allows the simulation of a large variety of manual exertions. This paper describes the status of the two different approaches and implications and requirements for future human simulation and modeling systems.

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.

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.

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.

Interference between physical objects in the workspace and the moving human body may cause serious problems, including errors in manual operation, physical damage and trauma from the collision, and increased biomechanical stresses due to movement reorganization for avoiding the obstacles. Therefore, a computer algorithm to detect possible collisions and simulate human motions to avoid obstacles will be an important tool for computer-aided ergonomics and optimization of system design in the early stage of a design process. In the present study, we present a method of modifying motions for obstacle avoidance when the object intrudes near the center of the planned motion. We take the motion modification approach, as we believe that for a certain class of obstacle avoidance problems, a person would modify a pre-planned motion that would result in a collision to a new one that is collision-free, as opposed to organizing a totally unique motion pattern.

Although biomechanics models can predict the stress on the musculoskeletal system, they cannot predict how the muscle load associated with exertion is perceived. The short-term goal of the present study was to model the perception of effort in lifting and reaching tasks. The long-term goal is to determine the correlation between objective and subjective measures of effort and use this information to predict fatigue or the risk of injury. Lifting and reaching tasks were performed in seated and standing situations. A cylindrical object and a box were moved with one hand and two hands, respectively, from a home location to shelves distributed in the space around the subject. The shoulder and torso effort required to perform these tasks were rated on a ten point visual analog scale.

Vehicle operators are required to perform a variety of reaching tasks while the vehicle is in motion. The vibration transmitted from the terrain-vehicle coupling can prevent the operator from successfully completing the required task. The level to which vibration inhibits the completion of these tasks must be more clearly understood in order to effectively design controls and displays that minimize these performance decrements. The Ride Motion Simulator (RMS) at the U.S. Army Tank-Automotive Research, Development, and Engineering Center (TARDEC) simulated single-axis and 6DOF ride motion, in which twelve participants were asked to perform push-button reaching tasks to eight RMS-mounted targets. In order to better ascertain the effects of dynamic ride motion on in-vehicle reaching tasks, we used a twelve-camera VICON optical motion capture system to record and UGS PLM Solutions’ Jack™ to analyze the associated kinematic and kinetic motions.

The object of this study has been to develop a quantitative description of the mobility of the human torso, including the shoulder girdle, neck, thoracic and lumbar vertebral column, and pelvis. This has been accomplished by a systematic multidisciplinary investigation involving techniques of cadaver dissection and measurement, utilizing cineradiofluoroscopy for joint center of rotation location, anthropometry, radiography, and photogrammetry for selected positions and motions of living subjects, and computer analysis. Positional and dimensional data were obtained for 72 anthropometric dimensions on 28 living male subjects statistically representative of the 1967 USAF anthropometric survey of 3542 rated officers, including bone lengths of the extremities and vertebral landmarks. Normal excursion of these limbs was measured in the living, utilizing the landmarks established in initial cadaver dissection.

This study begins the exploration of the relationship between shoulder external moments and perceived exertion levels for submaximal delivery tasks. Twenty subjects were recorded while performing hand load movement tasks to specified targets. After each exertion, subjects were asked to rate the effort required to perform the task. The recorded motion profiles were processed using a biomechanical upper extremity model, from which resultant external shoulder moments were calculated. Average resultant shoulder moments, stratified by exertion level, were also calculated. Several individual subject moment/exertion profiles showed identifiable trends. It was demonstrated that while no strong relationships exist for individual task exertion effort prediction based on resultant shoulder moments, there is a general trend in the overall data sample, as is shown by a high correlation between mean integrated resultant shoulder moment by exertion level and exertion levels.

Occupational populations have become increasingly diverse, requiring novel accommodation technologies for inclusive design. Hence, further attention is required to identify potential differences in work perception between workers with varying physical limitations. The major aim of this study was to identify differences in shoulder loading and perception of effort between a control population (C) and populations affected by chronic back pain (LBP) and spinal cord injury (SCI) in one-handed seated transfer tasks to targets. The effects of the injuries, and associated pain, are likely to produce variations in movement patterns, muscle loading and perceived effort.

Workers who must perform manual operations with the hands above shoulder level have increased risk of developing painful disorders of the neck and shoulder. This is a special problem in automobile assembly because of the trend toward unit body construction. Parts that operators formerly attached to a frame must now be assembled to the underbody structure. Using a special conveyor system to tilt the vehicle has been considered as a way to minimize musculoskeletal stress. Experimental trials of chassis assembly operations using a 30 degree tilt are described here. Tilting was not effective in reducing stress of overhead work. The 30 degree angle was insufficient to significantly reduce work height, and tilting frequently obscured, rather than improved, visibility. In order for tilting to be effective, it appears that a full 90 degree rotation is required, and the vehicle needs to be designed to take advantage of body tilting.

This book presents seven case studies in which digital human models were used to solve different types of physical problems associated with proposed human-machine interaction tasks. This book includes contributions from researchers at Ford, Boeing, DaimlerChrysler, General Motors, the U.S. Air Force, and others.

Digital human modeling (DHM) progress worldwide will be much faster and cohesive if the diverse community now developing simulations has a global blueprint for DHM, and is able to work together efficiently. DHM developers and users can save time by building on each other's work. This paper highlights a panel discussion on DHM goals and strategic plans for the next decade to begin formulating the international blueprint. Four subjects are chosen as the starting points: (1) moving DHM into the public safety and internet arenas, (2) role of DHM in computer assisted surgery and automotive safety, (3) DHM in defense applications, and (4) DHM to improve workplace ergonomics.

In this study, an angle-time-based motion modification method was developed. This method allows the use of existing motion data by modifying them to fit new scenarios given as new initial and final posture constraints. The motion modification method can generalize an existing motion data and derive, within a portion of space, a family of motions retaining the angular velocity characteristics of the original motion. It was found that the proposed method is capable of predicting realistic human motions with various new initial and final posture constraints in a robust manner. We expect that this motion modification method provides a way of using existing motion data more flexibly and economically.

A research effort was initiated to establish an empirical data base and to develop predictive models of normal human in-vehicle seated reaching motions while driving. A driving simulator was built, in which a variety of targets were positioned at typical locations a driver would possibly reach. Reaching motions towards these targets were performed by demographically representative subjects and measured by a state-of-the-art motion analysis system. This paper describes the experiment conducted to collect the movement data, and the new techniques that are being developed to process, analyze, and model the data. Some initial findings regarding the role of torso assistive motion, the effect of speed used in completing a motion on multi-segment dynamic postures, and illustrative results from kinematic modeling are presented.

Basic physical characteristics of the neck have been defined which have application to the design of biomechanical models, anthropometric dummies, and occupant crash protection devices. The study was performed using a group of 180 volunteers chosen on the basis of sex, age (18-74 years), and stature. Measurements from each subject included anthropometry, cervical range-of-motion (observed with both x-rays and photographs), the dynamic response of the cervical flexor and extensor muscles to a controlled jerk, and the maximum voluntary strength of the cervical muscles. Data are presented in tabular and graphic form for total range-of-motion, cervical muscle reflex time, decelerations of the head, muscle activation time, and cervical muscle strength. The range-of-motion of females was found to average 1-12 deg greater than that of males, depending upon age, and a definite degradation in range-of-motion was observed with increasing age.