Towards Machine Learning on Data from Professional Cyclists
Paper in proceedings, 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.


Agrin Hilmkil

Chalmers, Computer Science and Engineering (Chalmers), Formal methods

Oscar Ivarsson

Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd

Moa Johansson

Chalmers, Computer Science and Engineering (Chalmers), Formal methods

Dan Kuylenstierna

Chalmers, Microtechnology and Nanoscience (MC2), Microwave Electronics

Teun Van Erp

Free University of Amsterdam

World Congress on Performance Analysis in Sports
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Subject Categories

Computer and Information Science

Sport and Fitness Sciences

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