A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 1-Model Development

James Yang, Brad Howard, Juan Baus

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

OCCUPATIONAL APPLICATIONS: Digital human models have been widely used in occupational biomechanics assessments to prevent potential injury risks, such as automotive assembly lines, box lifting, patient repositioning, and the mining industry. Motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. We propose an algorithm that will ensure human motions are predicted realistically, and finally, use of this algorithm could help enhance the accuracy of injury risk assessments using digital human models.

Keywords

  • Cognitive theories
  • collision avoidance
  • optimization-based motion prediction
  • perceived risk theory

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