Study of bi-criterion upper body posture prediction using pareto optimal sets

R. Timothy Marier, Jingzhou Yang, Jasbir S. Arora, Karim Abdel-Malek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

This study involves further development of a direct approach to optimization-based posture prediction by using multi-objective optimization (MOO). Human performance measures representing joint displacement and delta potential energy are aggregated to predict more realistically, how virtual humans move. It is found that potential energy does not govern independently human posture. Rather, it must be coupled with another objective to avoid non-unique solutions and to improve realism. In any case, it is more suitable when reaching behind the avatar. Thus, we refine the idea of task-based posture prediction, concluding that performance measures should depend not only on the task being completed but also on where the task is completed relative to the human. Pareto optimal sets are depicted using the weighted sum and weighted min-max methods for MOO. By leveraging a special form of Pareto optimal set, insight is gained concerning how the functions should be combined. We find that the two MOO methods perform equally well, and the general form of the sets is independent of the target (to be touched with the finger) location.

Original languageEnglish
Title of host publicationProceedings of the Fifth IASTED International Conference on Modelling, Simulation, and Optimization
Pages229-234
Number of pages6
StatePublished - 2005
Event5th IASTED International Conference on Modelling, Simulation, and Optimization - Oranjestad, Aruba
Duration: Aug 29 2005Aug 31 2005

Publication series

NameProceedings of the IASTED International Conference on Modelling, Simulation, and Optimization
Volume2005

Conference

Conference5th IASTED International Conference on Modelling, Simulation, and Optimization
CountryAruba
CityOranjestad
Period08/29/0508/31/05

Keywords

  • Multi-objective
  • Optimization
  • Pareto optimal
  • Posture prediction

Fingerprint Dive into the research topics of 'Study of bi-criterion upper body posture prediction using pareto optimal sets'. Together they form a unique fingerprint.

  • Cite this

    Marier, R. T., Yang, J., Arora, J. S., & Abdel-Malek, K. (2005). Study of bi-criterion upper body posture prediction using pareto optimal sets. In Proceedings of the Fifth IASTED International Conference on Modelling, Simulation, and Optimization (pp. 229-234). (Proceedings of the IASTED International Conference on Modelling, Simulation, and Optimization; Vol. 2005).