A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 2-Application

James Yang, Brad Howard, Juan Baus

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Occupational Application: Digital human models have been widely used for occupational assessments to reduce potential injury risk, such as automotive assembly lines, box lifting, and in the mining industry. Human motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. An algorithm proposed earlier was implemented for human motion prediction, and simulated results were found to have a good correlation with the experimental studies. Use of this algorithm can help ensure that human motion is predicted realistically, and thus can impact the accuracy of injury risk assessments.

Original languageEnglish
Pages (from-to)211-222
Number of pages12
JournalIISE Transactions on Occupational Ergonomics and Human Factors
Volume9
Issue number3-4
DOIs
StatePublished - 2021

Keywords

  • Collision avoidance
  • optimization-based motion prediction
  • perceived risk theory

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