Intelligent Wearable Sensing for Remote Cardiovascular Monitoring Through Attention-Based Multisensor Learning
Keywords:
intelligent wearable sensing; attention-based multisensor learning; remote cardiovascular monitoring; health data infrastructure; algorithmic fairness; data governanceAbstract
Remote cardiovascular monitoring increasingly depends on wearable systems that collect heterogeneous physiological signals outside clinical settings. However, translating continuous multisensor data into clinically meaningful cardiovascular assessments poses substantial systems challenges. This paper presents a system-level examination of intelligent wearable sensing for remote cardiovascular monitoring through attention-based multisensor learning. Attention mechanisms offer adaptive weighting across sensor channels, enabling a system to suppress motion artifacts, temporal misalignment, and redundant modalities while amplifying physiologically salient features. The analysis emphasizes that effectiveness depends not only on model accuracy but also on sensing infrastructure, edge-cloud partitioning, security, data governance, fairness, and deployment sustainability. We examine structural trade-offs among energy consumption, latency, privacy preservation, and diagnostic interpretability. The discussion further addresses regulatory and policy implications, including data protection frameworks, algorithmic accountability, and equitable access across populations. By integrating prior developments in wearable sensing, deep learning, secure body sensor networks, and clinical artificial intelligence, the paper provides a forward-looking perspective on building robust, fair, and sustainable remote cardiovascular monitoring ecosystems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
