| Paper ID | MLSP-6.6 |
| Paper Title |
A SPARSE CODING APPROACH TO AUTOMATIC DIET MONITORING WITH CONTINUOUS GLUCOSE MONITORS |
| Authors |
Anurag Das, Seyedhooman Sajjadi, Bobak Mortazavi, Theodora Chaspari, Projna Paromita, Laura Ruebush, Nicolaas Deutz, Ricardo Gutierrez-Osuna, Texas A&M University, United States |
| Session | MLSP-6: Compressed Sensing and Learning |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation Time: | Tuesday, 08 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [SMDSP-SAP] Sparsity-aware processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 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. |
