Deep Learning-Based IoT Framework for Real-Time Health Monitoring

This paper presents an integrative design of a deep learning-based IoT framework for the purpose of health monitoring. The system utilizes both wearable technology and health cameras to receive basic health parameters from users on a permanent basis. The core part of the system is the enabling of the remote exchange of health data by means of a wireless connectivity relay network. The structure is meant to constantly measure advanced real-time vital signs to inform users on their health status in a timely manner. Critical serves the adjustment of the DNN (deep neural networks) complex deeply integrated to the system structure as well as the particular performance type of the DNN. The method of deep neural network (DNN) classification has been used to achieve these objectives. Algorithms are further refined after extraction to boost the effectiveness of the health predictive models as well as ever in use sophisticated and advanced devices. All these aid in creating a strong framework for a comprehensive real time health monitoring system.