| Title |
COVID-19 Detection using Disease Monitoring Systems based on Vital-signs from Smartwatch |
| Authors |
김진현(Jin Hyun Kim) ; 한용섭(Yongseop Han) ; 조형래(Hyeongrae Cho) ; 윤혜린(Hyerin Yoon) ; 김현수(Hyeonsu Kim) ; 구다예(Daye Gu) ; 강태신(Taeseen Kang) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.8.1197 |
| Keywords |
Medical IoT; Real-time monitoring; Medical time-series data; Anomaly detection |
| Abstract |
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. |