Abstract
Planning sport sessions automatically is becoming a very important aspect of improving an athlete’s fitness. So far, many Artificial Intelligence methods have been proposed for planning sport training sessions. These methods depend largely on test data, where Machine Learning models are built, yet evaluated later. However, one of the biggest concerns of Machine Learning is dealing with data that are not present in the training dataset, but are unavoidable for predicting the further improvements. This also represents a bottleneck in the domain of Sport Training, where algorithms can hardly predict the future training sessions that are compiled with the attributes of features presented in a training dataset. Usually, this results in an under-trained trainee. In this paper we look on this problem, and propose a novel method for synthetic data augmentation applied on the original dataset.
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References
Allen, H., Coggan, A.R., McGregor, S.: Training and Racing with a Power Meter, 3rd edn. VeloPress, Boulder (2019)
Banister, E.W.: Modeling elite athletic performance. Physiol. Test. Elite Athletes 347, 403–422 (1991)
Clark, M.A., Lucett, S.C., Sutton, B.G.: NASM Essentials of Personal Fitness Training, 4th edn. Jones & Bartlett Learning, Burlington (2014)
Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: AutoAugment: learning augmentation strategies from data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 113–123 (2019)
Fister, I., Fister Jr., I., Fister, D.: Computational Intelligence in Sports. ALO, vol. 22. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-03490-0
Fister, I., Rauter, S., Yang, X.-S., Ljubič, K., Fister Jr., I.: Planning the sports training sessions with the bat algorithm. Neurocomputing 149, 993–1002 (2015)
Fister Jr., I., Vrbancic, G., Brezočnik, L., Podgorelec, V., Fister, I.: SportyDataGen: an online generator of endurance sports activity collections. In: CECIIS: Central European Conference on Information and Intelligent Systems, pp. 171–178. IEEE (2018)
Frans, K., Ho, J., Chen, X., Abbeel, P., Schulman, J.: Meta learning shared hierarchies. arXiv preprint arXiv:1710.09767 (2017)
Friel, J.: The Cyclist’s Training Bible: The World’s Most Comprehensive Training Guide, 5th edn. VeloPress, Boulder (2018)
Goodfellow, I.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Iwana, B.K., Uchida, S.: An empirical survey of data augmentation for time series classification with neural networks. PLoS ONE 16(7), e0254841 (2021)
Jing, Y., Yang, Y., Feng, Z., Ye, J., Yizhou, Yu., Song, M.: Neural style transfer: a review. IEEE Trans. Visual. Comput. Graph. 26(11), 3365–3385 (2019)
Kauwe, S.K., Graser, J., Murdock, R., Sparks, T.D.: Can machine learning find extraordinary materials? Comput. Mater. Sci. 174, 109498 (2020)
Khalifa, N.E., Loey, M., Mirjalili, S.: A comprehensive survey of recent trends in deep learning for digital images augmentation. Artif. Intell. Rev., 1–27 (2021)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lemley, J., Bazrafkan, S., Corcoran, P.: Smart augmentation learning an optimal data augmentation strategy. IEEE Access 5, 5858–5869 (2017)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)
Rauter, S.: New approach for planning the mountain bike training with virtual coach (2018)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)
Silacci, A., Taiar, R., Caon, M.: Towards an AI-based tailored training planning for road cyclists: a case study. Appl. Sci. 11(1), 313 (2021)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Wang, J., Perez, L., et al.: The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Netw. Vis. Recogn. 11, 1–8 (2017)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016)
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Fister, I., Vrbančič, G., Podgorelec, V., Fister, I. (2022). Synthetic Data Augmentation of Cycling Sport Training Datasets. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2021. Lecture Notes in Networks and Systems, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-93247-3_7
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DOI: https://doi.org/10.1007/978-3-030-93247-3_7
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