Towards ergonomics working - machine learning algorithms and musculoskeletal modeling
Open Access
- 1 November 2021
- journal article
- Published by IOP Publishing in IOP Conference Series: Materials Science and Engineering
- Vol. 1208 (1)
- https://doi.org/10.1088/1757-899x/1208/1/012001
Abstract
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.This publication has 9 references indexed in Scilit:
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