Generative Adversarial Learning for Personalized Physiological Signal Denoising in Wearable Devices
Keywords:
generative adversarial networks; physiological signal denoising; wearable devices; personalization; federated learning; edge computing; fairness; data governanceAbstract
Wearable devices increasingly generate continuous physiological signals such as electrocardiograms, photoplethysmograms, electrodermal activity, and respiration. Although these signals hold promise for ambulatory health monitoring, their clinical and behavioral value is frequently degraded by motion artifacts, sensor displacement, environmental interference, and inter-individual variability. Conventional denoising methods often rely on population-level assumptions and stationary noise models, limiting their ability to adapt to individual physiological patterns and real-world conditions. Generative adversarial learning offers a flexible alternative because it learns to reconstruct plausible clean signals from corrupted observations while preserving subject-specific morphology. This article examines the system-level design of personalized physiological signal denoising using generative adversarial networks in wearable ecosystems. It emphasizes architectural trade-offs, data governance, edge-cloud infrastructure, robustness, fairness, sustainability, and regulatory implications rather than algorithmic details alone. The discussion covers conditional generative architectures, federated personalization, deployment strategies, demographic and sensor heterogeneity, model auditing, and lifecycle management. We argue that the successful translation of adversarial denoising into wearable practice requires an integrated socio-technical approach that connects modeling choices with clinical expectations, privacy constraints, energy budgets, and institutional oversight. The article provides an interdisciplinary synthesis that bridges artificial intelligence, wearable sensing, and health information infrastructure.
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