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.
This method reduces the labeling cost to train an efficient multi-task neural network by adding an additional clustering step in the multi-task active learning loop, within the sampling process
CUDLE is a self-supervised learning framework that improves cannabis use detection from wearable sensor data in real-world settings, outperforming supervised methods while requiring significantly fewer labeled samples.