An IoT-based smart healthcare system using location-based mesh network and big data analytics

Abstract

Elderly people requiring care the entire day usually depend on the availability of their family members to give assistance. However, the family members might not provide appropriate help especially in an emergent situation. The application of Internet of Things (IoT) technology with a variety of interconnected devices provides the solution. We propose an IoT-based smart healthcare system comprising wearable devices, which integrates a variety of contact sensors with location-based mesh networks (LBMN) such as Wi-Fi and Bluetooth Low Energy (BLE) connections to continuously sense various parameters of aging people. The BLE-connected devices such as wearable sensors, fixed sensors, seat cushions, pedal mats, magnetic reed switches, and mobile devices are all involved in collecting, processing, and transmitting physiological data and their locations to the cloud. Through the utilization of convenient interfaces such as software applications on smartphones and web pages on computers, it provides real time monitoring of the elderly in terms of localization, activity pattern, and health status. Thus the system enables early detection of health risks to the elderly. We used Platform as a service (PaaS) to receive and store the health data generated from the interconnected devices and to perform analysis. The essential feature of this LBMN is to generate a complete 6W(Who, What,When,Where,Why and How)big data for policy, feed it to the PaaS analysis to easily and quickly obtain more accurate data, and then develop possible health strategy or preventive measures. The proposed healthcare system detected that, out of the 20 participants recruited, 2 persons (10%) were often restless. It was also able to detect abnormal daily activity patterns with more tag positioning and the historical data from the devices. More importantly, it can help to prevent potential physical and neuropsychiatric disorders based on the real-time monitoring information and analyzed historical data for the aging people.