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Multibody System Dynamics

, Volume 32, Issue 1, pp 27–53 | Cite as

A hybrid dynamic motion prediction method for multibody digital human models based on a motion database and motion knowledge

  • Ilaria Pasciuto
  • Sergio Ausejo
  • Juan Tomás Celigüeta
  • Ángel Suescun
  • Aitor Cazón
Article

Abstract

In this paper, we present a novel method to predict human motion, seeking to combine the advantages of both data-based and knowledge-based motion prediction methods. Our method relies on a database of captured motions for reference and introduces knowledge in the prediction in the form of a motion control law, which is followed while resembling the actually performed reference motion. The prediction is carried out by solving an optimization problem in which the following conditions are imposed to the motion: must fulfill the goals of the task; resemble the reference motion selected from the database; follow a knowledge-based dynamic motion control law; and ensure the dynamic equilibrium of the human model, considering its interactions with the environment. In this work, we apply the proposed method to a database of clutch pedal depression motions, and we present the results for three predictions. The method is validated by comparing the results of the prediction to motions actually performed in similar conditions. The predicted motions closely resemble the motions in the validation database and no significant differences have been noted either in the motion’s kinematics or in the motion’s dynamics.

Keywords

Human motion prediction Digital human modeling Dynamic motion prediction Data-based motion prediction Knowledge-based motion prediction 

Notes

Acknowledgements

The authors would like to thank IFSTTAR for providing the database of captured motions used in the present study and Human Solutions GmbH for providing the RAMSIS specifications used to define the DHM. This research was partially funded by the European project DHErgo, “Digital Humans for Ergonomic design of products” (FP7/2007-2013), grant agreement \(\mathrm{n}^{\rm{o}}\) 218525. www.dhergo.org and by the Diputación Foral de Gipuzkoa, reference number 2/11.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Ilaria Pasciuto
    • 1
  • Sergio Ausejo
    • 1
  • Juan Tomás Celigüeta
    • 1
  • Ángel Suescun
    • 1
  • Aitor Cazón
    • 1
  1. 1.CEIT and TecnunUniversity of NavarraSan SebastianSpain

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