Abstract
Wearable electronics are often used for estimating the energy expenditure of the user based on heart rate measurement. While heart rate is a good predictor of calorie consumption at high intensities, it is less precise at low intensity levels, which translates into inaccurate results when estimating daily net energy expenditure. In this study, heart rate measurement was augmented with heat flux (HF) measurement, a form of direct calorimetry. A physical exercise test on a group of 15 people showed that HF measurement can improve the accuracy of calorie consumption estimates especially during rest and low-intensity activity when used in conjunction with heart rate information and vital background parameters of the user.
Highlights
T HE advent of wearable electronics has enabled consumers to measure their vital signs during their everyday life
In order to verify whether direct heat flux (HF) measurement has a positive effect on the accuracy of the EE estimate, the ordinary least squares (OLS) model (3) was tested using different combinations of input variables: heart rate (HR) only, HF only, HR and HF combined, HR and %RH combined, and, by combining HR, HF, and %RH
The key differences are seen in the distribution of the R2 scores; complementing the heart rate measurements with either HF or relative humidity measurements yields a notable decrease in variance of the R2 scores, with the best results obtained by combining HR, HF, and %RH
Summary
T HE advent of wearable electronics has enabled consumers to measure their vital signs during their everyday life. Devices, such as activity trackers, smart watches, and rings, are gaining ground in the measurement of biometric signals from the user. These devices contain one or more sensors for measuring signals, such as heart rate, skin temperature, humidity, and movement [1]. PPG, among other biometric measurements, gives the wearer the ability to track the level of their physical activity, which may be beneficial for applications such as sports performance monitoring. Nonsports-related applications, such as weight loss [3], sleep tracking [4], and health monitoring [5], have seen an increase in popularity
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