Estimate Power without Measuring it: a Machine Learning Application
Abstract
Power is the core metric of performance in cycling. Trainers strive for maximizing it while engineers strive for minimizing its loss by improving equipement and aerodynamic drag. However, costly and specific equipments are required to optimize this process. Here, we show that power can be estimated from side data without the need to acquire power data itself. More specifically, we show a proof of concept that machine learning can serve to estimate metrics with limited equipment or missing data. In this work, we limit the scope at estimating power but aerodynamic drag could be estimated in the same spirit.
Downloads
Published
How to Cite
Issue
Section
Copyright (c) 2020 Journal of Science and Cycling
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors contributing to Journal of Science and Cycling agree to publish their articles under a Creative Commons CC BY-NC-ND license, allowing third parties to copy and redistribute the material in any medium or format, and to remix, transform, and build upon the material, for any purpose, even commercially, under the condition that appropriate credit is given, that a link to the license is provided, and that you indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Authors retain copyright of their work, with first publication rights granted to Cycling Research Center.
