Nonparametric Model Prediction for Intelligent Regulation of Human Cardiorespiratory System to Prescribed Exercise Medicine

Abstract

Intelligent regulation for human exercise behaviors becomes significantly necessary for exercise medicine after the COVID-19 epidemic. The key issue of exercise regulation and its potential development for intelligent exercise is to describe human exercise physiological behaviors in a more accurate and sufficient manner. Here, a non-parametric modeling method with kernel-based regularization is presented to estimate cardiorespiratory biomarkers (i.e., oxygen uptake ( ${\dot {\text {V}}}$ O2) and carbon dioxide output ( ${\dot {\text {V}}}$ CO2) by merely non-invasively monitoring the indicator of exercise intensity (e.g., walking speed). Using the kernel-based non-parametric modeling, we show that ${\dot {\text {V}}}$ O2 and ${\dot {\text {V}}}$ CO2 behaviors in response to continuous and diversified exercise intensity stimulations can be quantitatively described. Furthermore, the dataset from the stairs experiment with a proper protocol is applied in the kernel parameter selection, and this selection approach is compared with the numerical simulation approach. The comparison results illustrate an improvement of 4.18% for oxygen uptake and 7.63% for carbon dioxide output in a half period, and 11.00% for oxygen uptake and 12.60% for carbon dioxide output in one period when using the kernel parameter selected from the stairs exercise. Moreover, the advantages of using the non-parametric model, the necessity of sufficient stimulation for identification and the importance of the kernel regularization term are also addressed in this paper. This method provides fundamental work for the practice of intelligent exercise.