Abstract
An analytical convolution-based model is used to predict a person’s physiological reaction to strain. Heart rate, oxygen uptake, and carbon dioxide output serve as physiological measures. Cycling ergometer tests of five male subjects are used to compare the proposed Convolution Model with a machine learning approach in form of a black box Wiener model. In these experiments, the Convolution Model yields smaller errors in prediction for all considered physiological measures. It performs very similar to other analytical models, but is based on only four parameters in its original form. A parameter reduction to one single degree of freedom is shown with comparable prediction accuracy and without significant loss of fitting accuracy.
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Notes
- 1.
MAPE values of each prediction in experiment A.2 are compared to MAPE values of each prediction in experiment A.3 with a two-sample t-test at \(\alpha =0.05\). Null hypothesis that data of both experiments comes from normal distributions with equal means and equal but unknown variances is rejected with \(p<0.01\).
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Acknowledgments
The authors gratefully thank Alexander Artiga Gonzalez and the research group around Dietmar Saupe from University of Konstanz, Germany, for providing the data set used in this paper.
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Ludwig, M., Grohganz, H.G., Asteroth, A. (2019). A Convolution Model for Prediction of Physiological Responses to Physical Exercises. In: Cabri, J., Pezarat-Correia, P., Vilas-Boas, J. (eds) Sport Science Research and Technology Support. icSPORTS icSPORTS 2016 2017. Communications in Computer and Information Science, vol 975. Springer, Cham. https://doi.org/10.1007/978-3-030-14526-2_2
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