Energy-Efficient Algorithms for Continuous Physiological Monitoring in Resource-Constrained Wearable Devices

Abstract

Continuous physiological monitoring through wearable devices has become central to precision health, remote care, and early clinical intervention. However, the promise of uninterrupted sensing is constrained by the severe energy, computational, communication, and memory limitations of miniature devices. This paper examines energy-efficient algorithms for continuous physiological monitoring from a system-level perspective, integrating architectural trade-offs, signal processing, adaptive sampling, embedded inference, and communication scheduling. Rather than presenting a single optimization technique, the discussion situates algorithmic efficiency within the larger infrastructure of wearable sensing, edge computing, and health data governance. The paper analyzes how dynamic power management, compressed sensing, and adaptive duty cycling interact with clinical reliability and device longevity. It further explores structural trade-offs among local processing, communication offloading, and cloud aggregation, arguing that energy efficiency cannot be separated from robustness, fairness, and regulatory compliance. Using examples including motion-tolerant photoplethysmography, electrocardiogram monitoring, and neural network compression, the analysis highlights how algorithmic decisions influence device sustainability and equitable health outcomes. The paper concludes that future wearable systems should be designed around adaptive, context-aware resource allocation mechanisms that reconcile clinical validity with energy constraints while embedding fairness and privacy considerations across the full deployment lifecycle. The review offers a cross-domain perspective for engineers, clinical informatics researchers, and policy stakeholders seeking to understand the design space of resource-constrained continuous monitoring.