A Real Time Healthcare Monitoring System Based on Open Source IoT and ANFIS

Abstract

Integrating the healthcare monitoring devices with various emerging technologies like wireless sensor networks and Internet of Things (IoT), has become a keen area of interest worldwide. The proposed system aims to develop a wearable wireless body sensor device integrating adaptive neural intelligence in the field of healthcare monitoring using IoT. Wireless body sensor units are able to detect behaviour of human body parameters and transmit them using various data analysis and transmission techniques. Adaptive Neural Fuzzy Inference System (ANFIS) would allow the system to prioritize the collected physiological parameters from the sensor nodes by itself, making it a smart healthcare monitoring system. The proposed model has been developed as a prototype of a real-time wearable e-healthcare monitoring system by integrating ANFIS and an open source IoT. The model consists of sensors that collects vital data from patient's body and then transmits using Wi-Fi to the Cloud service which can be accessed by any IoT platform (ThingSpeak) on central HUB. At central HUB, fuzzy logic converts raw data into linguistic variables which is trained in ANFIS to give priority to patients depending on the status of patient. This system results in providing a reliable, accurate and real time data of patients continuously, and transmits the prioritized data during emergency.