Review of “Contact Tracing Device for Workplace Using FAVORIOT Platform” by Siti Hajar Samsul and Wan Mahani Hafizah Wan Mahmud.
This paper presents a contact tracing device for workplaces using the FAVORIOT platform, which aims to minimize the spread of COVID-19 by monitoring close contacts. The authors use a BLE tag that combines ESP32 TTGO as a microcontroller and Bluetooth Low Energy (BLE) as a sensor to detect close contacts. The data collected from the BLE tags are transferred via an internet connection to the FAVORIOT platform, which processes and displays the information on a dashboard, enabling easy monitoring of the time interval of close contact and the number of active contacts.
The paper demonstrates the functionality of the BLE tag by testing it at various distances and recording the RSSI (Received Signal Strength Indicator) values. The authors found that an RSSI value of -55, equivalent to a distance of 1.2 meters, is suitable for detecting close contact. The analysis also revealed that obstacles, such as walls, could affect the detection of the RSSI values.
The BLE tag prototype is cost-efficient and compact and displays close contact names on an LCD screen. The FAVORIOT platform is an excellent database and an easy-to-monitor system when the module is switched on. The authors suggest that the system can be upgraded by adding a haptic buzzer to alert users about close contacts, improving its design, and implementing it in various applications. The proposed tool could also be used for contact tracing or large-scale monitoring in future health crises.
Overall, this paper provides a valuable contribution to the development of contact tracing devices, especially for workplace settings. The authors successfully demonstrate the feasibility of the BLE tag prototype and the FAVORIOT platform for monitoring close contacts. The suggested improvements and potential applications in future health crises make this work a good basis for further research and development in contact tracing technology.
You can download the original paper here.
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