Explainable Artificial Intelligence for Activity-Aware Cardiovascular State Assessment Using Wearable Sensors
Keywords:
explainable artificial intelligence, wearable sensors, cardiovascular state assessment, activity-aware inference, health data governance, deployment sustainability, fairness in clinical machine learningAbstract
Wearable sensors now support continuous cardiovascular monitoring outside traditional clinical settings, yet physical activity creates substantial complexity for interpreting cardiac signals. Motion artifact, autonomic adjustments, and sensor displacement can mimic or mask pathological changes, making activity-aware cardiovascular state assessment a difficult systems problem. This paper examines how explainable artificial intelligence can support robust and accountable cardiovascular inference in wearable environments. It argues that explainability must move beyond local feature attribution and become a structural property of the sensing, inference, deployment, and governance layers. The paper reviews advances in clinical cardiovascular deep learning, wearable activity recognition, and explainable artificial intelligence, then develops a system-level account of architectural trade-offs. Key issues include edge-cloud partitioning, multimodal fusion, activity context conditioning, uncertainty communication, data representativeness, fairness across demographic and physiological groups, and regulatory accountability. The discussion emphasizes that activity-aware explanation should clarify not only which physiological features influenced a prediction but also how motion context, signal quality, and population-specific factors shaped the inference. The paper further analyzes deployment infrastructure, sustainability, alert fatigue, privacy, and policy integration. It concludes that clinical value depends on designing systems that remain interpretable, robust under real-world activity, equitable across populations, and governable throughout their operational lifecycle.
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