Deep Learning based Smart Healthcare Monitoring System using Sensory Network

Abstract

The smart healthcare monitering systems are becoming popular day by day due to failure of traditional healthcare monitoring systems to provide real-time, accurate, and consistent monitoring of persons’s vital signs to provide medical treatment timely. The Internet of Things (IoT) make it possile to design and develop the smart healthcare system to do real-time patient monitoring. IoT plays vital role in monitring systems. This study predicts the person’s health using IoT sensor with advanced Deep Learning (DL) algorithms by consistent monitoring of person’s vital signs, such as heart rate, blood pressure, temperature, and oxygen level. Monitering of vital signs is very important to asses overall health and detect abnormalities The challenge of managing large-scale complex datasets from IoT devices is addressed by DL technology, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and artifical neural network (ANN) which analyze the data to detect abnormalities and provide high accuracy. Experimental results demonstrates the effectiveness of proposed approach for person’s health monitering. This research opens up new opportunities to integrate IoT and DL to improve clinical decision-making and patient care.