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Football players have a high risk of leg muscle injuries, especially when exposed to mental stress. Injuries to muscles of the thigh are common in amateur and professional football, representing almost a third of all injuries. These injuries occur primarily in non-contact situations and from overuse. They can lead to a range of costs, including financial costs associated with treatment as well as those associated with long-term recovery, and absence from training and/or competition. Further, there is a high risk of injury recurrence and subsequent injury.
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.