A Promising Smart Healthcare Monitoring Model based on Internet of Things and Deep Learning Techniques

Abstract

Using Internet of Things (IoT) and deep learning techniques in the healthcare sector has gained significant momentum in recent years. These technologies have the potential to transform traditional healthcare monitoring systems into real-time data collection, analysis, and decision-making capabilities. While several models have been developed to assist people with heart diseases, numerous obstacles still impede the effectiveness of the current solutions, such as power consumption, latency, accuracy, and scalability. Therefore, this study aims to develop a promising smart healthcare monitoring model that integrates IoT and deep learning techniques for saving patient lives. In addition, it clarifies the current research gap. The methodology used in this study was a literature review, which was conducted to identify relevant studies on IoT and deep learning applications in healthcare and find the gaps in each. This model consists of three main components: data acquisition through IoT devices, data processing using deep learning algorithms, and decision-making based on analyzed data. Moreover, it showed an unstable rate of accuracy in the current studies, which were taken from 2021 to 2023. In the future, our proposed smart healthcare monitoring model will solve that gap, which is already available in the current studies, and that will be proven using real-time materials such as Arduino and heart disease sensors.