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• W3216681444 abstract "Estimating the heart rate (HR) response to exercises of a given intensity without the need of direct measurement is an open problem of great interest. We propose here a model that can estimate the heart rate response to exercise of constant intensity and its subsequent recovery, based on soft computing techniques. Multilayer perceptron artificial neural networks (NN) are implemented and trained using raw HR time series data. Our model’s input and output are the beat-to-beat time intervals and the HR values, respectively. The numerical results are very encouraging, as they indicate a mean relative square error of the estimated HR values of the order of 10−4 and an absolute error as low as 1.19 beats per minute, on average. Our model has also been proven to be superior when compared with existing mathematical models that predict HR values by numerical simulation. Our study concludes that our NN model can efficiently predict the HR response to any constant exercise intensity, a fact that can have many important applications, not only in the area of medicine and cardio-vascular health, but also in the areas of rehabilitation, general fitness, and competitive sport." @default.
• W3216681444 created "2021-12-06" @default.
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• W3216681444 date "2021-12-02" @default.
• W3216681444 modified "2023-09-27" @default.
• W3216681444 title "A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises" @default.
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• W3216681444 doi "https://doi.org/10.3390/signals2040049" @default.
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