Using LSTM Networks and Future Gradient Values to Forecast Heart Rate in Biking

Henry Gilbert; Jules White; Quchen Fu; Douglas C. Schmidt

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Paper

Using LSTM Networks and Future Gradient Values to Forecast Heart Rate in Biking

Topics: Machine Learning in Sports Performance Prediction; Neural Networks and Big Data Analysis in Sports

In Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support icSPORTS - Volume 1, 53-60, 2022 , Valletta, Malta

Authors: Henry Gilbert ; Jules White ; Quchen Fu and Douglas C. Schmidt

Affiliation: Vanderbilt University, U.S.A.

Keyword(s): Deep LSTM, Deep Neual Network, Heart Rate, Forecasting, Biking.

Abstract: Heart Rate prediction in cycling potentially allows for more effective and optimized training for a given individual. Utilizing a combination of feature engineering and hybrid Long Short-Term Memory (LSTM) models, this paper provides two research contributions. First, it provides an LSTM model architecture that accurately forecasts the heart rate of a bike rider up to 10 minutes into the future when given the future gradient values of the course. Second, it presents a novel model success metric optimized for deriving a model’s accuracy to predict heart rate while an athlete is zone training. These contributions provide the foundations for other applications, such as optimized zone training and offline reinforcement models to learn fatigue embeddings.

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Paper citation in several formats:

Gilbert, H.; White, J.; Fu, Q. and Schmidt, D. (2022). Using LSTM Networks and Future Gradient Values to Forecast Heart Rate in Biking. In Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support - icSPORTS; ISBN Not Available; ISSN 2184-3201, SciTePress, pages 53-60. DOI: 10.5220/0011541800003321

@conference{icsports22,
author={Henry Gilbert. and Jules White. and Quchen Fu. and Douglas C. Schmidt.},
title={Using LSTM Networks and Future Gradient Values to Forecast Heart Rate in Biking},
booktitle={Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support - icSPORTS},
year={2022},
pages={53-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011541800003321},
isbn={Not Available},
issn={2184-3201},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support - icSPORTS
TI - Using LSTM Networks and Future Gradient Values to Forecast Heart Rate in Biking
SN - Not Available
IS - 2184-3201
AU - Gilbert, H.
AU - White, J.
AU - Fu, Q.
AU - Schmidt, D.
PY - 2022
SP - 53
EP - 60
DO - 10.5220/0011541800003321
PB - SciTePress