Reinforcement Learning-Based Adaptive Signal Quality Optimization for Wearable Health Monitoring
Keywords:
wearable health monitoring; reinforcement learning; signal quality; adaptive optimization; edge computing; algorithmic fairness; medical AI governanceAbstract
Wearable health monitoring systems increasingly operate as distributed, data-intensive infrastructures that support continuous physiological surveillance outside clinical environments. However, the quality of signals acquired by wearable devices remains highly dynamic because of motion artifacts, sensor displacement, environmental variability, and anatomical differences. This paper examines the use of reinforcement learning as a system-level mechanism for adaptive signal quality optimization in wearable health monitoring. Rather than treating signal conditioning as a static signal processing problem, the paper develops an infrastructure-oriented perspective that integrates sensor hardware, embedded computing, communication networks, cloud services, and governance arrangements. Reinforcement learning offers a principled approach to sequential decision making under uncertainty, enabling wearable platforms to learn context-dependent adjustments to sensing parameters, filtering strategies, and data transfer policies. The discussion addresses architectural trade-offs between edge autonomy and centralized orchestration, the challenges of training adaptive agents without compromising patient safety, and the implications of deploying learning systems across heterogeneous populations. Data governance, privacy, fairness, energy sustainability, and regulatory accountability are analyzed as constitutive elements of a responsible adaptive monitoring infrastructure. The paper further situates reinforcement learning within broader developments in artificial intelligence, edge computing, and digital twin modeling. It concludes that adaptive signal quality optimization should be understood not merely as a technical enhancement but as a socio-technical design problem that requires coordinated attention to robustness, explainability, equity, and regulatory maturity.
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