Abstract
Planning training sessions is one of the coaches’ main responsibilities in Sports Coaching. Coaches watch their athletes during training, identify key aspects of their performance that can be improved and plan training sessions to address the problems that they have observed. Limited work has been proposed and applied to the generation of training plans using technology. There is great potential for improving the generation of personalized training plans by using Machine Learning techniques. Recently, many methods and techniques were proposed in theory and practice in order to help athletes in sports training generally. Integrating some of these methods and techniques would result in the generation of automated, adaptive and personalized training plans. In this paper, we propose a conceptual framework for training plan generation in an adaptive and personalized way for athletes. This framework integrates performance indicators such as training load measures, physiological constraints, and behavior-change features like goal setting and self-monitoring. It provides a training plan, being adopted by the athlete, and its goal adapts to the athlete’s behavior.
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Zahran, L., El-Beltagy, M., Saleh, M. (2020). A Conceptual Framework for the Generation of Adaptive Training Plans in Sports Coaching. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_62
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DOI: https://doi.org/10.1007/978-3-030-31129-2_62
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