Allison Stephens

Anthropometry for a North American Manufacturing population.

Digital Human Models are used extensively in virtual manufacturing to evaluate hand clearance and reach. Spatial assessments of accommodation are typically conducted using digital human models representative of the manufacturing population. Unfortunately, these models are often based on anthropometry gathered from sources that are not representative of the actual target worker population. For example, the size and shape might be based on data from the U.S. military, which differs in fitness, age, and race distributions from the typical automotive manufacturing population. Ford ergonomists traced errors in accommodation predictions to these inaccurate representations. Using a recently developed statistical methodology incorporating principal components analysis, the anthropometry of the target worker population was synthesized. Using these new data, Ford updated the anthropometry of their digital human models to reflect changes due to secular trends in the U.S. The models also consider the diversity in age and race, thus producing Ford manikins that better represent the variability in their worker base. The improved ability to accurately predict accommodation allows for significant immediate and long-term reductions in engineering costs.

An Estimation of Supporting Hand Forces for Common Automotive Assembly Tasks

This study was designed to determine the forces applied to supporting hands, by automotive assembly operators, during common one-handed tasks such as hose installations or electrical connections. The data were computed as a percentage of body weight and a repeated measures analysis of variance (ANOVA) (p<0.05) was conducted. Supporting hand forces were observed to range from 5.5% to 12.1% of body mass across a variety of tasks. The results of this study can be used when performing a biomechanical/ergonomic analysis to account for these supporting hand forces.

The effect of manikin anthropometrics and posturing guidelines on proactive ergonomic assessments using digital human models

Preliminary investigations of the validity of manual digital human model (DHM) manipulations, to accurately reproduce real worker postures, have identified a potential need to refine the manual posturing strategies used by ergonomists when performing proactive virtual ergonomics assessments. This study investigated the effect of altering manikin anthropometrics and implementing posturing guidelines when using DHMs for this purpose. Twelve automotive assembly-line tasks were used to assess the differences between real worker postures, captured via motion capture techniques, and postures obtained from manually manipulated DHMs scaled to either an average female DHM or the real worker average height. Ergonomists performed the DHM assessments using a set of five postural guidelines. Using the postural guidelines generally resulted in more conservative estimates and, in some tasks, improved accuracy across kinetic (% capable, total solved force, L5-S1 compression force, and resultant shoulder torque) and kinematic (shoulder, elbow, and trunk joint angles, and shoulder and L5-S1 reach distances) variables.

Within and Between-Subject Reliability Using Classic Jack for Ergonomic Assessments

As the use of computer-aided ergonomic tools become more prominent in performing ergonomic evaluations early in the design phase, the drive to improve upon the validity, reliability, and accuracy of the technology will increase. Posturing a digital human (DH) in a virtual environment proves to be a challenging task. There are a very large number of possible positions in which the DH can be positioned for any given task, and the position that the DH is postured into may differ depending on the experiences of the user. This may lead to different conclusions regarding the acceptability of an operation.