# title
A SPARSE CODING APPROACH TO AUTOMATIC DIET MONITORING WITH CONTINUOUS GLUCOSE MONITORS

# abstract
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 meals 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 dictionarys 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.

# publicationOutlet
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021)

# nameOfConference
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

# authors[].organizations[].label
Texas A&M University Naresh K. Vashisht College of Medicine

# authors[].organizations[].label
College of Education and Human Development

# authors[].organizations[].label
Center for Translational Research in Aging and Longevity

# publisher.label
Institute of Electrical and Electronics Engineers (IEEE)

# authorList[]
Das, A., Sajjadi, S., Mortazavi, B., Chaspari, T., Paromita, P., Ruebush, L., Deutz, N., & Gutierrez-Osuna, R.