Low-Cost Multimodal Physiological Telemonitoring System through Internet of Things

Abstract

The objective of this study is to develop and test a patient telemonitoring system. This study was encouraged by the high number of health workers fatalities in Indonesia due to physical contact without proper protection. Based on the symptoms of COVID-19 it consists of electrocardiogram (ECG) sensors, body temperature sensors, respiratory rate sensors, and pulse oximeter. The physiological data were captured by the sensors and collected by a microcontroller then it sends the data to a cloud system so that health workers can access the data. The experiments were performed to test both the offline and online protocol to compare data sent via a direct connection and data sent via Wi-Fi. In the offline testing, there were several limitations observed such as the low sampling frequency of the ECG signals that reduce the fidelity of the signals. Such problems were also observed on respiratory rate data. Furthermore, the system is also very prone to subjects’ movement-related noise. The measurements of peripheral oxygen saturation (SpO2) and body temperature, on the other hand, have been detected the slight change up to 0.1% and 0.5oC respectively. In the online testing, the data transmission to the cloud is sent per 30 seconds so that morphologically the ECG signal data are not representative. The system requires a lot of improvements and future study should be directed to improve signals acquisition and processing while maintaining the concept of low-cost. Design improvement should also include a better attachment design to the human body as well as greater data transmission for the online system.