{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-machine-learning-on-data-from","title":"Towards Machine Learning on data from Professional Cyclists","arxiv_id":"1808.00198","date":"2018-08-01","proceeding":null,"authors":["Agrin Hilmkil","Oscar Ivarsson","Moa Johansson","Dan Kuylenstierna","Teun van Erp"],"abstract":"Professional sports are developing towards increasingly scientific training\nmethods with increasing amounts of data being collected from laboratory tests,\ntraining sessions and competitions. In cycling, it is standard to equip\nbicycles with small computers recording data from sensors such as power-meters,\nin addition to heart-rate, speed, altitude etc. Recently, machine learning\ntechniques have provided huge success in a wide variety of areas where large\namounts of data (big data) is available. In this paper, we perform a pilot\nexperiment on machine learning to model physical response in elite cyclists. As\na first experiment, we show that it is possible to train a LSTM machine\nlearning algorithm to predict the heart-rate response of a cyclist during a\ntraining session. This work is a promising first step towards developing more\nelaborate models based on big data and machine learning to capture performance\naspects of athletes.","url_abs":"http://arxiv.org/abs/1808.00198v1","url_pdf":"http://arxiv.org/pdf/1808.00198v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-machine-learning-on-data-from","repo_url":"https://github.com/agrinh/procyclist_performance","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}