Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises

TL;DR

This study demonstrates that machine learning models can accurately predict exercise load levels from heart rate features measured shortly after training, aiding real-life energy expenditure monitoring.

Contribution

It introduces a method to classify in-exercise energy loads using post-exercise heart rate features with high accuracy, highlighting the potential for practical applications.

Findings

Random forest achieved AUC of 0.88 with all features.

K-nearest neighbors reached AUC of 0.91 with 4 features.

Post-exercise heart activity retains information about in-exercise loads.

Abstract

The assessment of energy expenditure in real life is of great importance for monitoring the current physical state of people, especially in work, sport, elderly care, health care, and everyday life even. This work reports about application of some machine learning methods (linear regression, linear discriminant analysis, k-nearest neighbors, decision tree, random forest, Gaussian naive Bayes, support-vector machine) for monitoring energy expenditures in athletes. The classification problem was to predict the known level of the in-exercise loads (in three categories by calories) by the heart rate activity features measured during the short period of time (1 minute only) after training, i.e by features of the post-exercise load. The results obtained shown that the post-exercise heart activity features preserve the information of the in-exercise training loads and allow us to predict their actual in-exercise levels. The best performance can be obtained by the random forest classifier with all 8 heart rate features (micro-averaged area under curve value AUCmicro = 0.87 and macro-averaged one AUCmacro = 0.88) and the k-nearest neighbors classifier with 4 most important heart rate features (AUCmicro = 0.91 and AUCmacro = 0.89). The limitations and perspectives of the ML methods used are outlined, and some practical advices are proposed as to their improvement and implementation for the better prediction of in-exercise energy expenditures.