ABSTRACT
Quarantine is the process of restricting and separating the movement of people who have been exposed to a contagious disease to avoid proliferation. Quarantined subjects were monitored manually, and patients tended to abscond or 'run away.' In the Philippines, there was a lack of research and the absence of similar technology commercially available related to this matter. Project Bantay integrated Artificial Intelligence of Things (AIoT), indoor positioning systems, and wearable technology to alleviate the shortage of personnel, long-Term savings on workforce utilization in the government, and predict absconding or 'run-Away' potentially infectious individuals in our current and future quarantine facilities. Also, it included information system development for monitoring quarantined subjects' heart rates and temperatures that would eventually help the government combat and prepare for a similar unexpected pandemic. The prototype would start by turning on the device after being placed on the quarantined subject. The device must be linked to the router, and the sensors must then be calibrated. The web interface should receive and be able to see data readings, including temperature, heart rate, the location of the subject being quarantined, and the removal of the device via an IR reading. The temperature, heart rate sensor, and indoor positioning all measured above a p-value of 0.05, which accepted the null hypothesis, confirming that the actual and commercial product versus Project Bantay's temperature, heart rate, and indoor positioning accuracy was statistically the same. The anti-Absconding tendencies of the hypothetical dataset using machine learning data analytics showed that among four inherently multioutput machine learning regression algorithms, the Decision Tree Regression Algorithm could output a much better result in determining the tendencies of a subject to abscond from the quarantine facility. © 2022 IEEE.