TY - JOUR
T1 - A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision
T2 - Part 1-Model Development
AU - Yang, James
AU - Howard, Brad
AU - Baus, Juan
N1 - Publisher Copyright:
© 2021 “IISE”.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Cognitive theories
KW - collision avoidance
KW - optimization-based motion prediction
KW - perceived risk theory
UR - http://www.scopus.com/inward/record.url?scp=85115134296&partnerID=8YFLogxK
U2 - 10.1080/24725838.2021.1973613
DO - 10.1080/24725838.2021.1973613
M3 - Article
C2 - 34459361
AN - SCOPUS:85115134296
SN - 2472-5838
VL - 9
SP - 199
EP - 210
JO - IISE Transactions on Occupational Ergonomics and Human Factors
JF - IISE Transactions on Occupational Ergonomics and Human Factors
IS - 3-4
ER -