  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.
