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• W3007130207 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 AUC <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>micro</sub> = 0.87 and macro-averaged one AUC <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>macro</sub> = 0.88) and the k-nearest neighbors classifier with 4 most important heart rate features (AUC <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>micro</sub> =0.91 and AUC <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>macro</sub> =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." @default.
• W3007130207 created "2020-03-06" @default.
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• W3007130207 date "2019-12-01" @default.
• W3007130207 modified "2023-10-02" @default.
• W3007130207 title "Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises" @default.
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• W3007130207 doi "https://doi.org/10.1109/ssci44817.2019.9002970" @default.
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