Real-Time Health Monitoring System Using Deep Learning and IoT Integrated Electronics
Keywords:
Artificial Intelligence, Biomedical Sensors, Cloud Computing, Deep Learning, Edge Computing, Health Informatics, Internet of Things, Machine Learning, Real-Time Monitoring, Remote Patient Monitoring, Smart Healthcare, Wearable DevicesAbstract
The integration of Deep Learning (DL) and Internet of Things (IoT) with advanced electronics has revolutionized real-time health monitoring systems, enabling continuous, accurate, and non-invasive patient care. This research presents a novel framework that combines wearable IoT sensors with a deep learning model to collect and analyse vital physiological signals such as ECG, heart rate, blood oxygen saturation, and body temperature in real-time. The IoT devices transmit data securely via lightweight protocols like MQTT to cloud-based servers, where convolutional neural networks (CNN) enhanced with attention mechanisms automatically classify health conditions, including various arrhythmias and fever detection, with high accuracy. This approach eliminates manual feature extraction, ensuring robust detection and timely alerts for critical abnormalities, subsequently connecting the patient with healthcare professionals for immediate interventions. The system’s deployment demonstrates superior classification performance, with an accuracy exceeding 98%, surpassing existing models. Moreover, it addresses challenges including data reliability, privacy, and secure communications. The synergy of DL and IoT integrated electronics proves essential in facilitating scalable remote healthcare, reducing hospital visits, and promoting proactive medical management. This research highlights the transformative potential of such systems in advancing personalized and precision medicine globally.
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