Lightweight Deep Learning-Based Anomaly Detection for Resource-Constrained Embedded Systems

Continuous health monitoring has become increasingly important for preventive healthcare, remote patient observation, elderly care, and personal wellness applications. Conventional health monitoring architectures frequently depend on continuous communication with cloud platforms, which can introduce communication overhead, latency, privacy concerns, and increased energy consumption. This paper presents a low-power embedded system architecture for real-time health monitoring using edge intelligence. The proposed system integrates physiological sensors with a resource-constrained embedded controller to acquire vital parameters including heart rate, blood oxygen saturation, body temperature, and electrocardiogram signals. Local preprocessing is performed at the embedded device to remove noise and prepare the acquired signals for analysis. A lightweight edge intelligence module performs health-status classification and anomaly detection locally, thereby reducing dependence on cloud-based processing. The system incorporates an energy-aware operating mechanism based on adaptive sensing, duty cycling, and low-power processor operation. Wireless communication is used primarily for transmitting processed information, alerts, and selected health summaries rather than continuously transmitting raw physiological signals. The proposed architecture provides a unified framework combining physiological sensing, local signal processing, edge intelligence, wireless communication, and energy management. The architecture is suitable for wearable and portable health monitoring applications where low latency, reduced energy consumption, privacy, and continuous operation are important requirements.