A convolution model for prediction of physiological responses to physical exercises

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.