Computer Manikins in Evaluation of Manual Assembly Tasks

Doctoral thesis, 2009

Digital human modelling (DHM) tools have been introduced in industry to facilitate a faster and more cost efficient design process. The research in this thesis is directed towards the DHM-tool-users’ needs and the objective is to identify difficulties and shortcomings, and identify requirements for improving the output of DHM-simulations.The research is directed towards both the tool functions and the processes related to the use of the tools. Five studies are presented in the thesis. In the first study three examples of the development of DHM-based company-specific ergonomics evaluation methods and work processes are portrayed. These illustrate the use of DHM-tools for performing static work and occupant packaging analyses, as well as customizing activities made for the implementation of the tools in companies’ work processes. This is followed by a discussion of future needs of DHM-tools including the call for ergonomics methods for evaluating full work cycles. The second study shows that DHM-tools correctly predict ergonomics issues for standing and unconstrained working postures. However, for more complex and constrained working postures the tools must be used with caution to prevent an unlikely working posture from being the result/outcome of an ergonomics simulation. The third study confirms that posture differences are found in simulation results between users who carry out identical simulation cases. However, humans are different and tasks are carried out differently. Thus, simulation engineers should preferably simulate and visualize a number of different strategies when analysing work tasks. The fourth study illustrates how the appearance of a manikin used when showing and visually evaluating ergonomics conditions makes a difference. Knowledge in ergonomics and/or experience of making visual ergonomics posture evaluations decreases the appearance modes’ influences on the observers. The fifth study exemplifies a method where time sensitive wrist exposure data are extracted from a manikin’s wrist movements. The results show that the exemplified method makes it easy to compare simulations with data from avaliable field studies. However, to be able to use the data to predict prevalence for work-related musculoskeletal disorders additional research is needed to understand the different exposure-dimensions’ (position, velocity, rest) importance as epidemiological risk factors.