Analysis technique of a wearable IoT health information on the MAPHIS - Man Ko, Kwang; Gohn Kim, Soon (International Journal of Engineering and Technology, 2018)
Background/Objectives: As u-health becomes common that monitors body condition real time in the ubiquitous environment, people are increasingly interested in promoting their health using biometric information identified by various health equipment.Methods/Statistical analysis: the concept of digital health has emerged that encompasses the followings: u-health that is expected to improve efficiency in medical service and monitor patientsâ€™ condition with wireless communication through convergence of ICT and health care industry; smart health(s-health) that manages their own workout, calorie intake and sporting activities with their smart device; and, mobile health (m-health) that uses wearable and mobile devices as a means of healthcare.Findings: In this study, we aim to develop a health care platform that receives diabetes information generated from various IoT based on remote inputs, stores, analyzes, processes and provides visualized information. The purpose of this study is to develop and test IoT-based diabetes health big-data platform for diabetes mellitus patients. To achieve this goal, we suggest the development result of service and contents oriented â€œAn IoT-based diabetes health big-data Offloading platformâ€ that comprehensively manages healthcare products created in many IoT-based diabetes information to build a personal health management system. We also developed android 4.x-based application so that the health management service and contents provided by a third party can be checked with the client PC as well as health management service and contents offered by web-based client application and third party can be operated in the mobile environment such as smartphone or tablet.Improvements/Applications: The results of this study are verified by applying it to patients with diabetes or suspected cases. In order to increase the efficiency of real-time processing, we used off-loading technology to utilize big data related to diabetes generated from wearable IoT device. The results of this study will be used for telemedicine in two hospitals in Malaysia after various laboratory verification procedures.Â Â