BabyNutri

Abstract

The physical and physiological development of infants and toddlers requires the proper amount of macronutrient intake, making it an essential problem to estimate the macronutrient in baby food. Nevertheless, existing solutions are either too expensive or poor performing, preventing the widespread use of automatic baby nutrient intake logging. To narrow this gap, this paper proposes a cost-effective and portable baby food macronutrient estimation system, BabyNutri. BabyNutri exploits a novel spectral reconstruction algorithm to reconstruct high-dimensional informative spectra from low-dimensional spectra, which are available from low-cost spectrometers. We propose a denoising autoencoder for the reconstruction process, by which BabyNutri can reconstruct a 160-dimensional spectrum from a 5-dimensional spectrum. Since the high-dimensional spectrum is rich in light absorption features of macronutrients, it can achieve more accurate macronutrient estimation. In addition, considering that baby food contains complex ingredients, we also design a CNN nutrition estimation model with good generalization performance over various types of baby food. Our extensive experiments over 88 types of baby food show that the spectral reconstruction error of BabyNutri is only 5.91%, reducing 33% than the state-of-the-art baseline with the same time complexity. In addition, the nutrient estimation performance of BabyNutri not only obviously outperforms state-of-the-art and cost-effective solutions but also is highly correlated with the professional spectrometer, with the correlation coefficients of 0.81, 0.88, 0.82 for protein, fat, and carbohydrate, respectively. However the price of our system is only one percent of the commercial solution. We also validate that BabyNutri is robust regarding various factors, e.g., ambient light, food volume, and even unseen baby food samples.