Key result
A random forest regression model based on heart rate and respiratory rate accurately estimated metabolic rate (R2 = 0.99), outperforming the existing heart rate method.
Why the study?
Accurate measurement of metabolic rate currently requires complex or expensive equipment, limiting convenient real-time dynamic measurement during exercise.
Effect estimate: R2 0.99
A random forest regression model using heart rate and respiratory rate can accurately estimate dynamic metabolic rate (R² = 0.99), offering a convenient alternative to complex equipment.
Hypothesis-generating for wearable metabolic monitoring; requires prospective validation before clinical adoption.
This paper innovatively proposes a method to estimate metabolic rate. By extracting the time-domain statistical features of heart rate and respiratory rate, the metabolic rate evaluation model is established by using random forest regression algorithm. The results show that the model is superior to the existing heart rate method in predicting accuracy of metabolic rate and adapting to individual differences, and R 2 can reach 0.99. At present, the accurate measurement of metabolic rate requires complex or expensive equipment, but the method proposed in this paper is conducive to the real-time measurement of dynamic metabolic rate conveniently, which can be used to study energy expenditure during exercise.
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Zhang et al. (2021) studied this question. Random forest regression model based on heart rate and respiratory rate vs. Existing heart rate method was evaluated on Predicting accuracy of metabolic rate (R2 0.99). A random forest regression model based on heart rate and respiratory rate accurately estimated metabolic rate (R2 = 0.99), outperforming the existing heart rate method.
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