Use of multi-objective optimization for digital human posture prediction

R. Timothy Marler, Jasbir S. Arora, Jingzhou Yang, Hyung Joo Kim, Karim Abdel-Malek

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

With sufficient fidelity, the use of virtual humans can save time, money, and lives through improved product design, process design, and understanding of behaviour. Optimization-based posture prediction is a unique tool, and this article presents a study that advances posture prediction with a multi-objective optimization (MOO) approach. MOO is used to both develop and combine the following human performance measures: joint displacement; musculoskeletal discomfort; and a variation on potential energy. The following MOO methods are studied in the context of human modelling: objective sum; min-max; and global criterion. Using MOO yields realistic results. Of the independent performance measures, discomfort generally provides the most accurate postures. Potential energy, however, is not a significant factor in governing human posture and should be combined with other performance measures. The three MOO methods for combining performance measures yield similar results, but the objective sum provides slightly more realistic postures.

Original languageEnglish
Pages (from-to)925-943
Number of pages19
JournalEngineering Optimization
Volume41
Issue number10
DOIs
StatePublished - Oct 2009

Keywords

  • Human modelling
  • Multi-objective optimization
  • Posture prediction

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