A Context-Aware IoT-Based Smart Wearable Health Monitoring System

摘要

Development in wearable health monitoring technology has been dramatically improved due to the increasing use of wireless technologies and the miniaturization of electronic sensors. It is the potential to change the future of healthcare services through the use of active health monitoring devices on the Internet of Things (IoT) to track patients and athletes through their regular daily routines. Medical applications such as remote monitoring, biofeedback and telemedicine build a completely new framework for controlling health quality and costs. This work aims to develop a low-cost, high-quality multipurpose wearable smart device for tracking the health care of patients with heart disease and fitness athletes. In this paper, we discuss our proposed system through three phases. In the first phase, we use the Raspberry-Pi as an open-source microcontroller with a HealthyPi hat serving as a conduit between the Raspberry-Pi and the HealthyPi-connected biomedical sensors with different parameters such as temperature, ECG, pulse, oximetry, … etc. We started our experiment with 15 different test subjects with various gender, ages and levels of fitness. We positioned the proposed wearable device and gathered data on readings for each test subject when sitting, walking and running. The second phase includes linking our device to an open-source IoT dashboard to display the data via an interactive IoT dashboard to be accessed remotely by doctors, as well as introducing rules for action that send alerts to patients and doctors in the event of problems. We developed and tested a Fuzzy Logic system in the third phase, which inputs the data collected from the experiments on the accelerometer, gyroscope, heart rate and blood oxygen level, and provides the physical state (resting, walking or running) as output that helps to determine the patient/athlete's health status. The results obtained from the proposed method show efficient remote health status monitoring of test subjects in real-time through the IoT dashboard, and identification of anomalies in their health status, as well as effective detection of physical motion mode using the proposed Fuzzy Logic system design.