An Ultra Low Power Personalizable Wrist-Worn ECG Monitor Integrated with IoT Infrastructure

Abstract

Ultra-low-power physiological monitoring has emerged as a critical frontier in wearable health technologies as global healthcare shifts toward continuous preventive diagnostics, remote patient management, and decentralized monitoring systems. Wrist-worn ECG devices are central to this evolution, promising real-time cardiac surveillance without clinical dependence. Yet despite optimistic narratives, most commercially available wearables rely on high-power signal acquisition modules, proprietary algorithms, and cloud-dependent processing that create substantial barriers to sustained monitoring, user personalization, data ownership, and medical-grade accuracy. This study proposes and critically examines an ultra low-power personalizable wrist-worn ECG monitor integrated with an IoT infrastructure, illustrating how hardware optimization, lightweight signal-processing, embedded machine learning, and tiered data transmission can drastically reduce power consumption while enabling user-tailored cardiac analysis. Unlike traditional wearables that depend on continuous high-bandwidth transmission, the proposed system uses edge-level pre-processing, adaptive sampling, and energy-aware feature extraction to prolong battery duration without compromising diagnostic quality. The IoT layer introduces multi-tiered connectivity local BLE/Wi-Fi, gateway devices, and cloud-based analytics allowing scalable medical engagement while preserving user autonomy over ECG profiles and alert thresholds. Through a critical analysis grounded in existing scholarly and industrial literature, the paper argues that low-power wrist-worn ECG devices must be viewed not merely as hardware innovations but as socio-technical systems that reshape clinical workflows, patient agency, data accessibility, and decentralized care. Findings show that ultra-low-power ECG architectures offer transformative potential but only when transparency, personalization, and responsible IoT governance accompany technical efficiencies. The study concludes with a system-level evaluation and proposes pathways for equitable and scalable adoption in future cardiac health frameworks.