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Authors: Xiaoxing Qiu ; Jules White and Douglas C. Schmidt

Affiliation: Department of Computer Science, Vanderbilt University, Nashville, TN 37212, U.S.A.

Keyword(s): Heart Rate Forecasting, Personalization, Cycling Course, Deep Learning.

Abstract: Heart rate forecasting in cycling is most effective when it is personalized and course-specific to account for the influence of individual and terrain factors. This paper empirically assesses various personalized and course-specific heart rate forecasting models based on four machine learning models, including random forest, feed forward neural networks (FFNNs), recurrent neural networks (RNNs), and long short term memory (LSTM). The mean square error (MSE) is selected as the metric for model comparison. The results of our experiments show that despite the severely overfitted random forest models the LSTM models have the lowest MSE in the heart rate forecasting on our test dataset.

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Paper citation in several formats:
Qiu, X.; White, J. and Schmidt, D. (2021). A Study of Machine Learning Models for Personalized Heart Rate Forecasting in Mountain Biking. In Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - icSPORTS; ISBN 978-989-758-539-5; ISSN 2184-3201, SciTePress, pages 87-94. DOI: 10.5220/0010630600003059

@conference{icsports21,
author={Xiaoxing Qiu. and Jules White. and Douglas C. Schmidt.},
title={A Study of Machine Learning Models for Personalized Heart Rate Forecasting in Mountain Biking},
booktitle={Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - icSPORTS},
year={2021},
pages={87-94},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010630600003059},
isbn={978-989-758-539-5},
issn={2184-3201},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Sport Sciences Research and Technology Support - icSPORTS
TI - A Study of Machine Learning Models for Personalized Heart Rate Forecasting in Mountain Biking
SN - 978-989-758-539-5
IS - 2184-3201
AU - Qiu, X.
AU - White, J.
AU - Schmidt, D.
PY - 2021
SP - 87
EP - 94
DO - 10.5220/0010630600003059
PB - SciTePress

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