Measuring motion capture data quality for data driven human motion synthesis

In automotive industry, market demands shorter life cycles and individualized products. For manual assembly, this trend leads to more frequent planning of an ever increasing number of process variants. In order to ensure planning quality, virtual verification of manual production is crucial for efficient process optimization. However, virtual verification is not established in practice because available simulation tools require prohibitive manual modeling effort for human motions of acceptable quality. For automating the modeling process, data driven motion synthesis approaches are promising candidates that -however- require high quality input data for acceptable synthesis results. Therefore, objective motion capture data quality measures for data driven human motion synthesis are sought. This work proposes and tests a principal component analysis (PCA) and a Shannon entropy based quality measure. Both measures evaluate post-processed data and thus consider motion capture hardware in combination with a post-processing tool chain. The measures are tested for selectivity and validity using two low cost and two high cost motion capture systems. They differ in selectivity for high and low cost motion capture systems. Both measures correctly predict motion synthesis quality in tests with treadmill walking. Therefore, they can be employed for testing if a motion capture system is suitable for data driven motion synthesis that relies on PCA for input dimension reduction. Further research on robustness of the measures against motion variation is proposed. (C) 2015 The Authors. Published by Elsevier B.V.