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MealMeter: Using Multimodal Sensing and Machine Learning for Automatically Estimating Nutrition Intake
MealMeter: Using Multimodal Sensing and Machine Learning for Automatically Estimating Nutrition Intake

MealMeter is a linear regression based technique applied on multi-modal data collected using a CGM sensor and a wristband and tracks meal macronutrients. MealMeter achieves as low as 0.37 average root mean squared relative errors (RMSRE) in carb tracking, which is at least 15.9% improvement compared to TabPFN foundational model and other baselines.

Apr 7, 2025
LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction
LEAD: Localized Explanations with Adversarial Decision Boundary Characterization for Interpretable Disease Prediction

LEAD is an explainable AI technique that determines relative feature contributions by characterizing the decision boundary and perturbing critical samples along the decision boundary close to the test sample. LEAD achieves at least 6% improved fidelity and 7% improved consistency compared to LIME and SHAP.

Apr 7, 2025