Design a Simple Portable Electrocardiograph Based on the IOT Concept

Abstract

Abstract There are many cases of heart disease with death associated. The symptoms are not always clearly visible and require examination using an electrocardiogram (ECG) device to be detected. Recording human electrical activity can be done in various organs of the human body, one of which is in the heart. An ECG is a medical test to detect electrical activity signals produced by the heart by exchanging the signals on a monitor or graph on paper. This study aimed to design, build, test, and analyze a portable ECG device that is compact and easy to use. This portable electrocardiogram helps monitor human heart rate to check heart rate periodically. Tool design is done by making tools equipped with a monitoring system. The proposed system uses the AD8232 sensor to detect a person’s cardiac activity. Arduino Nano is a microcontroller connected to ESP32 with a WiFi feature. Heart activity monitors can be done anywhere by applying the Internet of Things concept, namely with the Ubidots application. A portable tool using Li-Ion batteries as a tool voltage source and BMS 3S as a charger, this tool is also equipped with a 3.2-inch TFT screen as a graph viewer of the results of monitoring heart activity. The results showed that the system could monitor the user’s heart for 5 minutes when sitting, walking, and running. The system created has been able to send heart signal data to be processed by Arduino Nano and ESP32 as a data transfer tool so that the data generated in the form of electrocardiogram graphs can be displayed on the Ubidots application. TFT LCDs can display electrocardiogram graphs generated by the electrical activity of the human heart and beats per minute values. The AD8232 sensor can measure beat per minute (bpm) values with an average increase in bpm values for walking conditions of 17% and running conditions of 19.17%. The AD8232 Sensor Performance can detect the user’s heart rate with a percentage of data difference between walking and running conditions of 2.88% with an accuracy rate of 97.12%.