Optimization-based posture reconstruction for digital human models

Jared Gragg, Aimee Cloutier, James Yang

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Digital human modeling provides a valuable tool for designers when implemented early in the design process. Motion capture experiments offer a means of validation of the digital human simulation models. However, there is a gap between the motion capture experiments and the simulation models, as the motion capture results are marker positions in Cartesian space and the simulation model is based on joint space. Therefore, it is necessary to map the motion capture data to simulation models by employing a posture reconstruction algorithm. Posture reconstruction is an inherently redundant problem where the collective distance error between experimental joint centers and simulation joint centers is minimized. This paper presents an optimization-based method for determining an accurate and efficient solution to the posture reconstruction problem. The procedure is used to recreate 120 experimental postures. For each posture, the algorithm minimizes the distance between the simulation model joint centers and the corresponding experimental subject joint centers which is called the mean measurement error.

Original languageEnglish
Pages (from-to)125-132
Number of pages8
JournalComputers and Industrial Engineering
Volume66
Issue number1
DOIs
StatePublished - 2013

Keywords

  • Digital human models
  • Motion capture
  • Optimization
  • Posture reconstruction
  • Validation

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