Towards ergonomics working - machine learning algorithms and musculoskeletal modeling

  • Ergonomic workplaces lead to fewer work-related musculoskeletal disorders and thus fewer sick days. There are various guidelines to help avoid harmful situations. However, these recommendations are often rather crude and often neglect the complex interaction of biomechanical loading and psychological stress. This study investigates whether machine learning algorithms can be used to predict mechanical and stress-related muscle activity for a standardized motion. For this purpose, experimental data were collected for trunk movement with and without additional psychological stress. Two different algorithms (XGBoost and TensorFlow) were used to model the experimental data. XGBoost in particular predicted the results very well. By combining it with musculoskeletal models, the method shown here can be used for workplace analysis but also for the development of real-time feedback systems in real workplace environments.

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Author:Franz Suess, Maximilian MelznerORCiD, Sebastian DendorferORCiD
Parent Title (English):IOP Conference Series: Materials Science and Engineering
Publisher:IOP Publishing
Document Type:conference proceeding (article)
Year of first Publication:2021
Release Date:2021/12/13
Article Number:012001
Corresponding author: Sebastian Dendorfer
Konferenzangabe:13th International Conference on Development and Modernization of the Manufacturing (RIM 2021), 29th September - 1st October 2021, Sarajevo, Bosnia and Herzegovina
Institutes:Fakultät Maschinenbau
Regensburg Center of Biomedical Engineering - RCBE
Regensburg Center of Health Sciences and Technology - RCHST
Fakultät Maschinenbau / Labor Biomechanik (LBM)
research focus:Lebenswissenschaften und Ethik
OpenAccess Publikationsweg:Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium
Corresponding author der OTH Regensburg
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International