Towards Machine Learning on Data from Professional Cyclists
Paper i proceeding, 2018

Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition to heart-rate, speed, altitude etc. Recently, machine learning techniques have provided huge success in a wide variety of areas where large amounts of data (“big data”) is available. In this paper, we perform a pilot experiment on machine learning to model physical response in elite cyclists. As a first experiment, we show that it is possible to train a LSTM machine learning algorithm to predict the heart-rate response of a cyclist during a training session. This work is a promising first step towards developing more elaborate models based on big data and machine learning to capture performance aspects of athletes.

Författare

Agrin Hilmkil

Chalmers, Data- och informationsteknik, Formella metoder

Oscar Ivarsson

Administration DoIT

Moa Johansson

Chalmers, Data- och informationsteknik, Formella metoder

Dan Kuylenstierna

Chalmers, Mikroteknologi och nanovetenskap (MC2), Mikrovågselektronik

Teun Van Erp

Vrije Universiteit Amsterdam

World Congress on Performance Analysis in Sports
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Idrottsvetenskap

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2019-01-11