Abstract
As training plans of athletes are static and are not adapted dynamically towards current conditions (e.g. lack of sleep, hunger, weather, and stress), this contribution depicts an expert system to close this gap. Based on an extensive literature research, relevant parameters that influence sport performance, and explicit effects in the form of rules are detected. An empirical study that we conducted with more than 100 athletes revealed significance levels of certain parameters and further rules. Taking into account this data, a prototype Android application using a rule-based expert system is presented. While some parameters such as weather and location are determined automatically, the remaining parameters are collected by self-assessment that users have to do before and after the workout. All data (also including sensor data during workout) is stored in a database. In future, machine learning technologies will be included in order to derive new rules based on user statistics.
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Acknowledgement
We would like to thank TBI Technologie-Beratungs-Institut GmbH for funding University of Rostock’s sub-project Train4U. Furthermore, TrainOXY™ is financially supported by the European Union under the European Regional Development Fund (ERDF), Mecklenburg-Western Pomerania’s Operational Programme 2014–2020. Special thanks go to our project partners OXY4 GmbH, and Faculty of Engineering, Department Mechanical Engineering/Process and Environmental Engineering, University of Wismar for the work on the cooperative project.
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Waßmann, I., Graf von Malotky, N.T., Martens, A. (2020). Train4U - Mobile Sport Diagnostic Expert System for User-Adaptive Training. In: Lames, M., Danilov, A., Timme, E., Vassilevski, Y. (eds) Proceedings of the 12th International Symposium on Computer Science in Sport (IACSS 2019). IACSS 2019. Advances in Intelligent Systems and Computing, vol 1028. Springer, Cham. https://doi.org/10.1007/978-3-030-35048-2_10
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