Abstract
Improving motion capture processing onto a high-fidelity digital human model is an important research area. Although there has been significant research in this field, little work has been done to determine posture and anthropometry simultaneously with the intent of visualizing the data on high-fidelity human models. Many existing techniques are less accurate when applying processed data to a digital model for biomechanical analysis. This paper presents a novel approach that estimates posture and anthropometry using optimization-based posture prediction to determine joint angles and link-lengths of a digital human. By including anthropometric design variables, this approach introduces flexible handling of innate variance in subject-model measurements without need for pre- or post-processing. This produces a more realistic motion and exhibits anthropometric measurements closer to those of the original subject, resulting in a new level of biomechanical accuracy that allows for analysis of a processed motion with a higher degree of confidence.
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Acknowledgements
This work is funded by the US Office of Naval Research, under program W911QY-12-C-0009. The authors would like to thank the team at the University of Iowa’s Virtual Soldier Research program. This work was completed at The University of Iowa, where the authors worked at the time.
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Seydel, A. et al. (2018). Improved Motion Capture Processing for High-Fidelity Human Models Using Optimization-Based Prediction of Posture and Anthropometry. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2017. Advances in Intelligent Systems and Computing, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-60591-3_50
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DOI: https://doi.org/10.1007/978-3-319-60591-3_50
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