Covid-19 detection using disease monitoring systems based on vital-signs from smartwatch

Real-Time vital-sign from patients are important information that implies the current health status and behavior of patients. Recently, Mishra et al. [1] have shown that COVID-19 can be detected by analyzing the patient's vital signs and behaviors, i.e., heart rates and steps, using anomaly detection techniques. This paper presents a medical IoT platform, called MiT Eco-platform, which is designed to gather patient's physiological data through a smartwatch and to increase the efficiency of data labeling for building an AI model for medical diagnosis and treatment. Furthermore, we present a real-time COVID-19 detection approach advanced from the approach of using anomaly detection Mishra et al. [1] that will be run on MiT Eco-platform. As a result, we show performance evaluation results of preemptively detecting the COVID-19 infection for the same samples of the COVID-19 infected ones of Mishra et al.[1], comparing with the anomaly detection approach of Mishra et al.[1]. We expect that physiological data through smartwatches on daily life can be continuously gathered and effectively labeled by the MiT Eco-platform for various studies in medical area. © 2021 Korean Institute of Electrical Engineers. All rights reserved.