Enabling Self‐Powered Physiological Monitoring with a Deep Learning‐Driven Wearable Piezoelectric Patch

The growing demand for continuous, real‐time health monitoring has made the development of self‐powered wearable devices increasingly important for tracking various physiological parameters. This study introduces a novel self‐powered piezoelectric patch that integrates a deep learning‐based system for continuous physiological monitoring. The patch incorporates a potassium sodium niobate (KNN)‐based piezoelectric nanogenerator (PENG), which efficiently harvests energy from diverse body movements, such as walking, running, and finger tapping, while also serving as a sensor to measure key health indicators, including pulse rate and heartbeat. Constructed from a flexible composite film of KNN nanoparticles and PDMS, the device ensures mechanical flexibility, durability, and biocompatibility. Real‐time activity recognition and physiological parameter classification are achieved through a convolutional neural network (CNN) that processes the voltage signals generated by the PENG. The self‐powered feature eliminates the need for external energy sources, enabling long‐term, low‐maintenance operation. This research article demonstrates the integration of piezoelectric energy harvesting with machine learning, showcasing the potential for personalized health monitoring, fitness tracking, and medical diagnostics. The findings highlight the promise of these devices in advancing wearable technologies that are efficient, sustainable, and responsive to individual health needs.