Use of multi-objective optimization for digital human posture prediction

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.

Keywords:

• multi-objective optimization
• human modelling
• posture prediction

Acknowledgements

This research was funded by the US Army TACOM project, Digital Humans and Virtual Reality for Future Combat Systems, and the Caterpillar project, Digital Human Modelling and Simulation for Safety and Serviceability.