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Title: A Sparse Coding Approach to Automatic Diet Monitoring with Continuous Glucose Monitors
Measuring dietary intake is a major challenge in the management of chronic diseases. Current methods rely on self-report measures, which are cumbersome to obtain and often unreliable. This article presents an approach to estimate dietary intake automatically by analyzing the post-prandial glucose response (PPGR) of a meal, as measured with continuous glucose monitors. In particular, we propose a sparse-coding technique that can be used to estimate the amounts of macronutrients (carbohydrates, protein, fat) in a meal from the meal’s PPGR. We use Lasso regularization to represent the PPGR of a new meal as a sparse combination of PPGRs in a dictionary, then combine the sparse weights with the macronutrient amounts in the dictionary’s meals to estimate the macronutrients in the new meal. We evaluate the approach on a dataset containing nine standardized meals and their corresponding PPGRs, consumed by fifteen participants. The proposed technique consistently outperforms two baseline systems based on ridge regression and nearest-neighbors, in terms of correlation and normalized root mean square error of the predictions.  more » « less
Award ID(s):
2014475
PAR ID:
10295237
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Page Range / eLocation ID:
2900 to 2904
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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