TY  - JOUR
PY  - 2022//
TI  - Mitigating the risk of musculoskeletal disorders during human robot collaboration: a reinforcement learning approach
JO  - Proceedings of the Human Factors and Ergonomic Society annual meeting
A1  - Xie, Ziyang
A1  - Lu, Lu
A1  - Wang, Hanwen
A1  - Su, Bingyi
A1  - Liu, Yunan
A1  - Xu, Xu
SP  - 1543
EP  - 1547
VL  - 66
IS  - 1
N2  - 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. <br><br>RESULTS of our experiments confirm that the proposed method can significantly improve the workers? postures.<p />  <p>Language: en</p>
LA  - en
SN  - 2169-5067
UR  - http://dx.doi.org/10.1177/1071181322661151
ID  - ref1
ER  -