Journal article
Use of multi-objective optimization for digital human posture prediction
Engineering optimization, Vol.41(10), pp.925-943
10/01/2009
DOI: 10.1080/03052150902853013
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.
Details
- Title: Subtitle
- Use of multi-objective optimization for digital human posture prediction
- Creators
- R. Timothy Marler - University of IowaJasbir S Arora - University of IowaJingzhou Yang - Texas Tech UniversityHyung-Joo Kim - Hyundai MotorsKarim Abdel-Malek - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Engineering optimization, Vol.41(10), pp.925-943
- Publisher
- Taylor & Francis
- DOI
- 10.1080/03052150902853013
- ISSN
- 0305-215X
- eISSN
- 1029-0273
- Language
- English
- Date published
- 10/01/2009
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Mechanical Engineering; Civil and Environmental Engineering
- Record Identifier
- 9984196520902771
Metrics
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