Citation

Xie Z, Lu L, Wang H, Su B, Liu Y, Xu X. Proc. Hum. Factors Ergon. Soc. Annu. Meet. 2022; 66(1): 1543-1547.

Copyright

(Copyright © 2022, Human Factors and Ergonomics Society, Publisher SAGE Publishing)

DOI

10.1177/1071181322661151

PMID

unavailable

Abstract

Work-related musculoskeletal disorders (MSDs) are often observed in human-robot collaboration (HRC), a common work configuration in modern factories. In this study, we aim to reduce the risk of MSDs in HRC scenarios by developing a novel model-free reinforcement learning (RL) method to improve workers? postures. Our approach follows two steps: first, we adopt a 3D human skeleton reconstruction method to calculate workers? Rapid Upper Limb Assessment (RULA) scores; next, we devise an online gradient-based RL algorithm to dynamically improve the RULA score. Compared with previous model-based studies, the key appeals of the proposed RL algorithm are two-fold: (i) the model-free structure allows it to ?learn? the optimal worker postures without need any specific biomechanical models of tasks or workers, and (ii) the data-driven nature makes it accustomed to arbitrary users by providing personalized work configurations.

RESULTS of our experiments confirm that the proposed method can significantly improve the workers? postures.

Language: en