LSM-2: Self-Supervised Learning for Incomplete Wearable Sensor Data in Health Monitoring

Wearable devices have revolutionized health monitoring by providing continuous, multimodal physiological and behavioral data — from heart signals and sleep patterns to activity levels and stress indicators.

Due to advances in sensor technology, it is increasingly feasible to capture a large volume of data, but the cost of labeling remains high, requiring real-time user annotations or laborious clinical studies.

Self-supervised learning (SSL) addresses this limitation by directly using the unlabeled data to learn underlying structures, such as subtle physiological relationships.

When applied at scale, SSL can enable the creation of foundation models that produce rich and generalizable representations useful for a wide variety of downstream health tasks.