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Predicting Heart Rates Of Sport Activities Using Machine Learning

Govers, Ruben (2021) Predicting Heart Rates Of Sport Activities Using Machine Learning.

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Abstract:Predicting heart rates for cycling exercise is useful for a more efficient planning workout and estimating nutrition intake. This is a difficult problem that is influenced by both internal factors such as the persons physical condition and external factors such as the weather. The goal of the research is to predict heart rate zones for new users for bicycle rides. Two problems are defined. The first problem is to find an optimal regression model trained on a set of bicycle rides and their average features. The best performing model was a random forest regressor with feature selection through random feature elimination. The second problem is to predict the heart rate on the time sequence data of these bicycle rides, where each sequence or segment denotes 100 meters. This is done by training a LSTM. The LSTM was capable of predicting heart rate averages for segments, but struggled with peaks and under- and overestimation.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science BSc (56964)
Link to this item:http://purl.utwente.nl/essays/85685
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