Improving Remote Cardiovascular Care with Wearable Data: Algorithms, Study Design, and Subject-Specific Adaptation

Cardiovascular diseases remain the leading cause of death worldwide, yet early detection and continuous monitoring remain challenging outside clinical settings. This dissertation is motivated by the growing potential of remote health monitoring to address this gap—specifically, the use of consumer-grade smartwatches to track cardiovascular health through physiological signals. Although consumer-grade wearables are traditionally merely used as fitness-oriented or recreational, this work investigates the clinical applicability of smartwatch-derived signals for disease monitoring in real-world, non-clinical environments. By enabling scalable, data-driven detection of cardiovascular conditions in everyday settings, such a system has the potential to reduce the burden on physicians, provide patients with continuous insights, and alleviate pressure on healthcare systems through earlier intervention and more personalized care.

By assessing how far wearable-based research has progressed toward operational deployment and identify critical shortcomings in real-world utility and generalizability, we confront several major challenges intrinsic to this domain: the medical interpretability of noisy consumer-grade signals, high inter-subject variability, and the inherent complexity of timeseries data that varies with context (e.g., day/night cycles, physical activity).

Our solution strategy is grounded in machine learning techniques that aim to learn robust, transferable representations of physiological data. In particular, we explore contrastive learning, weak supervision, and morphological modeling—such as acceleration-deceleration curve analysis— as tools to extract clinically relevant patterns. These methods are evaluated across both publicly available and proprietary datasets to ensure applicability to diverse populations.

By addressing these challenges, this dissertation advances the case for smartwatches as viable tools for longitudinal, data-efficient cardiovascular monitoring, contributing to a future in which early detection of conditions like atrial fibrillation and heart failure is feasible at scale in everyday settings.