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Towards Virtual Assessment of Human Factors: A Concept for Data Driven Prediction and Analysis of Physical User-product Interactions

Part of: User-Centred Design

Published online by Cambridge University Press:  26 July 2019

Alexander Wolf ,
Nicole Binder ,
Jörg Miehling  and
Sandro Wartzack

Abstract

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The early consideration of human factors in product development hugely favours the development of products, which excel with a positive user experience. The virtual environment of product development however, still has significant gaps in the virtual assessment and simulation of human factors, especially for user-product interactions composed of human movements. This motivates us to introduce a concept for data-driven prediction and analysis of user-product interactions. Heart of the concept is a predictive component that models the interaction between the user, represented by a musculoskeletal model, and the product, represented by product characteristics. We describe the implementation of this concept based on a pilot study for a lifting task. Motion capturing was performed to build a database and compare the results of our novel approach. The resulting kinematic and dynamic quantities show similar curve profiles with a small constant offset to the measured data. This indicates that the concept enables the virtual comparison of different designs or concepts regarding human factors.

Keywords

User centred designSimulationEarly design phasesUser-product interactionData-driven movement prediction
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 4029 - 4038
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Ackermann, M. and van den Bogert, A.J. (2010), “Optimality principles for model-based prediction of human gait”, Journal of biomechanics, Vol. 43 No. 6, pp. 1055–1060.10.1016/j.jbiomech.2009.12.012Google Scholar
Andersen, M.S., Damsgaard, M., MacWilliams, B. and Rasmussen, J. (2010), “A computationally efficient optimisation-based method for parameter identification of kinematically determinate and over-determinate biomechanical systems”, Computer methods in biomechanics and biomedical engineering, Vol. 13 No. 2, pp. 171–183.Google Scholar
Andersen, M.S., Damsgaard, M. and Rasmussen, J. (2009), “Kinematic analysis of over-determinate biomechanical systems”, Computer methods in biomechanics and biomedical engineering, Vol. 12 No. 4, pp. 371–384.Google Scholar
Bubb, H. (2015), Automobilergonomie, ATZ / MTZ-Fachbuch, Springer Vieweg, Wiesbaden.Google Scholar
Bubb, H., Engstler, F., Fritzsche, F., Mergl, C., Sabbah, O., Schaefer, P. and Zacher, I. (2006), “The development of RAMSIS in past and future as an example for the cooperation between industry and university”, International Journal of Human Factors Modelling and Simulation, Vol. 1 No. 1, p. 140.Google Scholar
Damsgaard, M., Rasmussen, J., Christensen, S.T., Surma, E. and Zee, M.d. (2006), “Analysis of musculoskeletal systems in the AnyBody Modeling System”, Simulation Modelling Practice and Theory, Vol. 14 No. 8, pp. 1100–1111.Google Scholar
Farahani, S.D., Andersen, M.S., de Zee, M. and Rasmussen, J. (2016), “Optimization-based dynamic prediction of kinematic and kinetic patterns for a human vertical jump from a squatting position”, Multibody System Dynamics, Vol. 36 No. 1, pp. 37–65.Google Scholar
Fluit, R., Andersen, M.S., Kolk, S., Verdonschot, N. and Koopman, H.F.J.M. (2014), “Prediction of ground reaction forces and moments during various activities of daily living”, Journal of biomechanics, Vol. 47 No. 10, pp. 2321–2329.10.1016/j.jbiomech.2014.04.030Google Scholar
Gunduz, M. and Yetisir, T. (2018), “A design reuse technology to increase productivity through automated corporate memory system”, Neural Computing and Applications, Vol. 29 No. 9, pp. 609–617.Google Scholar
Holden, D., Saito, J. and Komura, T. (2016), “A deep learning framework for character motion synthesis and editing”, ACM Transactions on Graphics, Vol. 35 No. 4, pp. 1–11.Google Scholar
Miehling, J., Krüger, D. and Wartzack, S. (2013), “Simulation in Human-Centered Design – Past, Present and Tomorrow”, In: Abramovici, M. and Stark, R. (Ed.), Smart Product Engineering, Lecture Notes in Production Engineering, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 643–652.Google Scholar
Miehling, J., Schuhhardt, J., Paulus-Rohmer, F. and Wartzack, S. (2015), “Computer Aided Ergonomics Through Parametric Biomechanical Simulation”, Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2015, Boston, Massachusetts, USA, Sunday 2 August 2015.Google Scholar
Peng, X.B., Abbeel, P., Levine, S. and van de Panne, M. (2018), “DeepMimic. Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills”, ACM Transactions on Graphics, Vol. 37 No. 4, pp. 1–14.Google Scholar
Rasmussen, J. (2005), “Musculoskeletal Simulation – (Dis)comfort Evaluation”, S&V OBSERVER, pp. 8–9.Google Scholar
Seeger, H. (2005), Design technischer Produkte, Produktprogramme und -systeme: Industrial Design Engineering, 2., bearb. und erw. Aufl., Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg.Google Scholar
Vajna, S., Weber, C., Zeman, K., Hehenberger, P., Gerhard, D. and Wartzack, S. (2018), CAx für Ingenieure: Eine praxisbezogene Einführung, SpringerLink Bücher, 3., vollständig neu bearbeitete Auflage, Springer Vieweg, Berlin, Germany.Google Scholar
Vapnik, V.N. (2000), The Nature of Statistical Learning Theory, Statistics for Engineering and Information Science, Second Edition, Springer, New York, NY.10.1007/978-1-4757-3264-1Google Scholar
Wagner, D.W., Reed, M.P. and Rasmussen, J. (2007), “Assessing the Importance of Motion Dynamics for Ergonomic Analysis of Manual Materials Handling Tasks using the AnyBody Modeling System”, SAE Congress: Digital Human Modeling for Design and Engineering (DHM).Google Scholar
Wolf, A. and Wartzack, S. (2018), “Parametric movement synthesis. Towards virtual design optimistaion of man-machine interaction in engineering design”, Design 2018: Proceedings of the 15th International Design Conference, May 2018, Dubrovnik, Croatia, May, 21-24, 2018, pp. 941–952.10.21278/idc.2018.0400Google Scholar
Zhang, L., Helander, M.G. and Drury, C.G. (2016), “Identifying Factors of Comfort and Discomfort in Sitting”, Human Factors: The Journal of the Human Factors and Ergonomics Society, Vol. 38 No. 3, pp. 377–389.Google Scholar