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It is generally accepted that all postures obtained from motion capture technology are realistic and accurate. Physical props are used to enable a subject to interact more realistically within a given virtual environment, yet, there is little data or guidance in the literature characterizing the use of such physical props in motion capture studies and how these effect the accuracy of postures captured. This study was designed to evaluate the effects of various levels of physical prop complexity on the motion-capture of a wide variety of automotive assembly tasks. Twenty-three subjects participated in the study, completing twelve common assembly tasks which were mocked up in a lab environment. There were 3 separate conditions of physical props: Crude, Buck, and Real. The Crude condition provided very basic props, or no props at all, while the Buck condition was a more elaborate attempt to provide detailed props. Lastly, the Real condition included real vehicle sections and real parts.

Automotive manufacturing presents unique challenges for ergonomic analysis. The variety of tasks and frequencies are typically not seen in other industries. Moving these challenges into the realm of digital human modeling poses new challenges and offers the opportunity to create and enhance tools brought over from the traditional reactive approach. Chiang et al. (2006) documented an enhancement to the Siemen's Jack Static Strength Prediction tool. This paper will document further enhancements to the ErgoSolver (formerly known as the Ford Static Strength Prediction Solver).

Assembly operators are rarely observed performing one-handed tasks where the unutilized hand is entirely inactive. Therefore, 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 to account for these supporting hand forces when performing a biomechanical/ergonomic analysis.

Due to the challenges in quantifying hand loads in manufacturing environments it is often assumed that the load is evenly distributed between the hands, even when handling parts with non-uniform mass distribution. This study estimated hand loads for six female subjects, when handling a custom part in 8 different configurations (2 weights, 4 CofM locations). The calculated hand loads varied from 20 to 50% of the weight being handled. The magnitude of asymmetrical hand loading depended on both the part orientation and the location of the CoM. Based on this study the knowledge of part weight, CofM location and hand positioning will allow the users of digital human models to perform more realistic and reliable task analysis assessments as the force distributions will be more representative of the actual loading rather than simply assuming the load is evenly distributed between the hands.

Often, ergonomists need to determine the maximum acceptable load or force for a given task. Ergonomic tools, like the NIOSH Lifting Guidelines (Waters et al, 1993) and the Liberty Mutual Tables (Snook & Ciriello, 1991)), provide such loads for selected population percentiles. In contrast, the UGS Jack Static Strength Prediction tool (JSSP), based on the University of Michigan’s 3D Static Strength Prediction Program (3DSSPP), uses force(s) as inputs and calculates the percentage of the male or female population that would be capable (%Cap) for a given task. Typically, the %Cap threshold will be a fixed number determined from corporate or government guidelines (e.g. 75% of females). Thus, in order to find the acceptable load, users of JSSP must iterate through loads until they find a %Cap that is just below their predetermined threshold.