Abstract
Millions of children die from preventable injuries every year around the world. Environmental modification is one of the most effective ways to prevent these fatal injuries. The environment should be modified and products should be designed in ways that will reduce the risk of injury by taking child–environment and child–product interactions into account. However, it is still very difficult even for advanced simulation systems to predict how children interact with products in everyday life situations. In this study, we explored a data-driven method as a promising approach for simulating children’s interaction with products in everyday life situations. We conducted an observational study to collect data on children’s climbing behavior and developed a database on children’s climbing behavior to clarify a climbing configuration space, which enables the prediction and simulation of the possible climbing postures of children.
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References
Heron, M.: Deaths: leading causes for 2014. Natl. Vital Stat. Rep. 65(5), 17 (2016)
Sminkey, L.: World report on child injury prevention. Inj. Prev. 14(1), 69 (2008)
Jung, K., Kwon, O., You, H.: Development of a digital human model generation method for ergonomic design in virtual environment. Int. J. Ind. Ergon. 39(5), 744–748 (2009)
Yang, J., Kim, J.H., Abdel-Malek, K., Marler, T., Beck, S., Kopp, G.R.: A new digital human environment and assessment of vehicle interior design. Comput.-Aided Des. 39(7), 548–558 (2007)
Farooq, A., Won, C.S.: A survey of human action recognition approaches that use an RGB-D sensor. EIE Trans. Smart Process. Comput. 4(4), 281–290 (2015)
Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: Computer Vision and Pattern Recognition (2017)
Bengio, Y.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
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This paper is partially supported by a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
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Nose, T., Kitamura, K., Oono, M., Nishida, Y., Ohkura, M. (2019). Automatic Learning of Climbing Configuration Space for Digital Human Children Model. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2018. Advances in Intelligent Systems and Computing, vol 780. Springer, Cham. https://doi.org/10.1007/978-3-319-94223-0_46
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DOI: https://doi.org/10.1007/978-3-319-94223-0_46
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