NeuroErgo: A Deep Neural Network Method to Improve Postural Optimization for Ergonomic Human-Robot Collaboration
Atieh Merikh Nejadasl, Omid Gheibi, Greet Van de Perre, Bram Vanderborght
Abstract
Collaborative robots can help industry workers to improve their ergonomics. They can propose a safe and ergonomic posture to the workers to reduce the risk of musculoskeletal disorders. Proposing an ergonomic stance needs postural evaluation and optimization. To optimize the workers' posture, we need to run the optimization on a cost function representing the ergonomic status. The tabular ergonomic assessment methods are the most common methods used by ergonomists, but they are linear stepwise functions that are not differentiable and not suitable for optimization purposes. We propose NeuroErgo, a deep neural network model that can approximate the tabular ergonomic assessment methods more precisely than existing methods. By solving the task constraints optimization problem for any task in industry and NeuroErgo as posture cost function, a safe and ergonomic posture can be derived and recommended to the workers while accomplishing their job.
BibTeX
@inproceedings{icra2022_neuroergoadeepne,
title = {NeuroErgo: A Deep Neural Network Method to Improve Postural Optimization for Ergonomic Human-Robot Collaboration},
author = {Atieh Merikh Nejadasl and Omid Gheibi and Greet Van de Perre and Bram Vanderborght},
booktitle = {ICRA 2022},
year = {2022}
}