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5th International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2021 ; : 78-83, 2021.
Article in English | Scopus | ID: covidwho-1537690

ABSTRACT

Coronavirus Disease (COVID-19) is an infectious disease caused by SARS-CoV-2, a newly discovered coronavirus. Infected people experience mild symptoms such as colds, which would lead to severe respiratory-related diseases that weaken the entire body. As the virus can be transmitted primarily through direct contact with an infected person or by touching surfaces and objects contaminated by respiratory droplets, it is critical to control and cut the transmission routes by exposing individuals under quarantine. In this study, the researchers developed an Arduino-based ankle tracker, associated with an application as the GUI, to implement geofencing to monitor people under home quarantine. The ankle tracker acts as the trigger being monitored within a geofence, a circular virtual perimeter. The system was designed to have a 95% confidence level to filter out inaccurate GNSS location data. It will only accept data that is highly accurate which depends on the PDOP value. The Geo-mapping accuracy of the tracker device was tested and presented in this study. © 2021 IEEE.

2.
5th International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2021 ; : 128-133, 2021.
Article in English | Scopus | ID: covidwho-1537689

ABSTRACT

COVID-19 signs are similar to flu and their symptoms can range from no signs and symptoms (asymptomatic) to severe signs and symptoms (symptomatic). Fever, cough, fatigue, runny or stuffy nose, body aches, conjunctivitis, and headache are some of the symptoms that COVID-19 and flu have in common. Current technologies only take advantage of thermal sensors to identify the individual with possible flu-like symptoms. Adding extra layer of security in preventing the spread of diseases for people with flu-like symptoms must be set up at key locations using a video camera. The study can detect facial features such as reddish eyes, runny nose, dark circles around eyes and measure temperature using a non-contact thermal camera. Gathered image datasets were used for model training. Initial testing with the system revealed that closer distance and better illumination yielded better results upon consultation with a doctor using the comparison of 100 LUX and 1000 LUX lighting conditions. Validations were done using live video feeds where PCA and SVM were used for feature extraction and classification respectively. Support Vector Machine was used to evaluate subject whether they exhibited flu-like symptoms or not and compare the system output with doctors diagnosis. Error rates of 26.67% and 50% were achieved for False Acceptance and False Rejection Rates respectively along with 0% error rates for the temperature detection system. © 2021 IEEE.

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