Continuous glucose traces look like one noisy curve. They are not. They contain a slow baseline and short-term deviations from meals, activity, and sensor artifacts. GlucoFM is built around that split.

The model is a lightweight, self-supervised foundation model for continuous glucose monitoring. One stream tracks slower metabolic state. The other tracks transient events. Time of day is preserved. Missing readings are masked rather than invented. Pretraining predicts latent structure instead of reconstructing raw noise, and uses augmentations that mimic real device failures: drift, sparse sampling, brief disconnects.

Traditional stethoscopes and commercial MEMS acoustic sensors have limited frequency range, making it hard to capture the full spectrum of physiological signals (from very low-frequency respiration and pulse waves to high-frequency heart sounds and vocalizations) in a single wearable device.

Researchers have developed AusculPatch — a lightweight, flexible, wireless auscultation patch that uses a novel cantilever pressure transducer with a nanothin cantilever and tiny airgap. This design enables broadband sensing from 0.2 Hz to over 10 kHz in one compact, low-power device (~3.2 g, 4.5 mW).

A new AI framework turns routine retinal imaging into a powerful noninvasive window for simultaneous detection of multiple metabolic and endocrine disorders.

The system combines quality-aware modules with frozen foundation models to deliver accurate, interpretable predictions from standard eye scans in under 30 seconds.