Evaluating the iterative development of VR/AR human factors tools for manual work.

This paper outlines the approach taken to iteratively evaluate a set of VR/AR (virtual reality / augmented reality) applications for five different manual-work applications - terrestrial spacecraft assembly, assembly-line design, remote maintenance of trains, maintenance of nuclear reactors, and large-machine assembly process design - and examines the evaluation data for evidence of the effectiveness of the evaluation framework as well as the benefits to the development process of feedback from iterative evaluation. ManuVAR is an EU-funded research project that is working to develop an innovative technology platform and a framework to support high-value, high-knowledge manual work throughout the product lifecycle. The results of this study demonstrate the iterative improvements reached throughout the design cycles, observable through the trending of the quantitative results from three successive trials of the applications and the investigation of the qualitative interview findings. The paper discusses the limitations of evaluation in complex, multi-disciplinary development projects and finds evidence of the effectiveness of the use of the particular set of complementary evaluation methods incorporating a common inquiry structure used for the evaluation - particularly in facilitating triangulation of the data.