Research and Development of an IoT-Based Remote Asthma Patient Monitoring System.

This paper presents the research and development of an Internet of Things- (IoT-) based remote health monitoring system for asthmatic patients. Asthma is an inflammatory disease. Asthma causes the lungs to swell and get narrower, making it difficult to carry air in and out of the lungs. This situation makes breathing very difficult. Remote patient monitoring (RPM) is a method of collecting health-related data from patients who are in a remote location and electronically transmitting it to healthcare providers for evaluation and consultation. The aim of this study is to design a monitoring system that allows doctors to monitor asthmatic patients from a remote area. The proposed system will allow patients to measure oxygen saturation (SpO2), heart rate, body temperature, humidity, volatile gases, room temperature, and electrocardiogram (ECG) using various sensors, which will be displayed in an application. This data is then sent to the doctor to monitor the patient's condition and suggest appropriate actions. Overall, the system consists of an Android application, a website, and various sensors. The Android studio and Java programming language were used to develop the application. For the frontend, the website was built using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), JavaScript, and jQuery. The system also uses Django, a Python-based open-source web framework, for the backend. The system developed the various sensors using an ESP8266 microcontroller compatible with the Arduino Integrated Development Environment (IDE). The system uses a MAX30100 pulse oximeter and heart rate sensor, a GY-906 MLX90614 noncontact precision thermometer, a DHT11 humidity and temperature sensor, a MQ-135 gas and air quality sensor, and an AD8232 ECG sensor for collecting various parameters that may trigger asthma attacks. Finally, the system developed the Asthma Tracker app and the Asthma Tracker website for remote health monitoring. The system was initially tested on demo patients and later deployed and tested on seven real human test subjects. Overall, the monitoring system produced satisfactory results. The data acquired by the sensors has a high level of accuracy. The system also maintained user-friendliness and low cost.