SIMONIC: IoT Based Quarantine Monitoring System for Covid-19
COVID-19, which has become a global pandemic since March 2020, has tremendously affected human life globally. The negative impact of COVID-19 affects societies in almost all aspects. Implementing quarantine monitoring, also social distancing, and contact tracing are a series of processes that can suppress the new infected COVID-19 cases in various countries. Prior works have proposed different monitoring systems to assist the monitoring of individuals in quarantines, as well as many methods are offered for social distancing and contact tracing. These methods focus on one function to provide a reliable system. In this paper, we propose IoT-based quarantine monitoring by implementing a geofence equipped with social distancing features to offer an integrated system that provides more benefits than one system carrying one particular function. We propose a system consisting of a low cost, low complexity, and reusable wristband design and mobile apps to support the quarantine monitoring system. For the geofencing, we propose a GPS-based geofence system that was developed by taking advantage of the convenience offered by the Traccar application. Meanwhile, we add the notification for social distancing feature with adaptive distance measurement RSSI-based set up in the android application. Based on the experiment we did to validate the system, in terms of wristband-to-smartphone communication, scanning interval in smartphone and advertising interval in wristband is best to set in 7 s for both. For social distancing notification and geofence, we measure the system performance through precision, recall, accuracy, and F-measure.
“World health organization.” https://www.who.int/ (accessed Dec. 28, 2021).
H. Ryu, A. Abulali, and S. Lee, “Assessing the effectiveness of isolation and contact-tracing interventions for early transmission dynamics of covid-19 in South Korea,” IEEE Access, vol. 9, pp. 41456–41467, 2021, doi: 10.1109/ACCESS.2021.3064371. Crossref
“Quarantine in Japan.” https://quarantine-japan.com/ (accessed Dec. 28, 2021).
G. Jaswal, R. Bharadwaj, K. Tiwari, D. Thapar, P. Goyal, and A. Nigam, “AI-biometric-driven smartphone app for strict post-COVID home quarantine management,” IEEE Consum. Electron. Mag., vol. 10, no. 3, pp. 49–55, 2021, doi: 10.1109/MCE.2020.3039035. Crossref
J. Tan, E. Sumpena, W. Zhuo, Z. Zhao, M. Liu, and S. G. Chan, “IoT geofencing for COVID-19 home quarantine enforcement,” IEEE Internet of Things Mag., vol. 3, no. 3, pp. 24-29, Sep. 2020, doi: 10.1109/IOTM.0001.2000097. Crossref
Y. C. Hou, M. Z. Baharuddin, S. Yussof, and S. Dzulkifly, “Social distancing detection with deep learning model,” 2020 8th Int. Conf. Inf. Technol. Multimedia, 2020, pp. 334–338, doi: 10.1109/ICIMU49871.2020.9243478. Crossref
M. E. Rusli, S. Yussof, M. Ali, and A. A. Abobakr Hassan, “MySD: A smart social distancing monitoring system,” 2020 8th Int. Conf. Inf. Technol. Multimedia, 2020, pp. 399–403, doi: 10.1109/ICIMU49871.2020.9243569. Crossref
A. Narzullaev, Z. Muminov, and M. Narzullaev, “Contact tracing of infectious diseases using Wi-Fi signals and machine learning classification,” 2020 IEEE 2nd Int. Conf. Artif. Intell. Eng. Technol., 2020, doi: 10.1109/IICAIET49801.2020.9257812. Crossref
O. Ruan, T. Liu, and D. Zhou, “Efficient and privacy-preserving of COVID-19 contact tracing scheme,” 2020 Int. Conf. Comput. Sci. Manag. Technol., 2020, pp. 105–108, 2020, doi: 10.1109/ICCSMT51754.2020.00028. Crossref
W. Tan and J. Liu, “Application of face recognition in tracing COVID-19 fever patients and close contacts,” 2020 19th IEEE Int. Conf. Mach. Learn. Appl., 2020, pp. 1112–1116, doi: 10.1109/ICMLA51294.2020.00179. Crossref
C. Sonia Villamizar, G. Edwar Jacinto, and A. Holman Montiel, “Design of an electronic bracelet for remote surveillance of people deprived of their freedom in Colombia,” Int. J. Appl. Eng. Res., vol. 12, no. 24, pp. 15452–15457, 2017.
M. F. Monir, A. H. Chowdhury, R. Anzum, and M. A. Amin, “IoT enabled geofencing for covid-19 home quarantine,” 2021 8th Int. Conf. Comput. Commun. Eng., 2021, pp. 373–378, doi: 10.1109/ICCCE50029.2021.9467204. Crossref
E. R. Pratama, F. Renaldi, F. R. Umbara, and E. C. Djamal, “Geofencing technology in monitoring of geriatric patients suffering from dementia and alzheimer,” 2020 3rd Int. Conf. Comput. Informatics Eng., 2020, pp. 106–111, doi: 10.1109/IC2IE50715.2020.9274637. Crossref
J. Helmy and A. Helmy, “Demo abstract: Alzimio: A mobile app with geofencing, activity-recognition and safety features for dementia patients,” 2017 IEEE Conf. Comput. Commun. Workshop, 2017, pp. 994–995, doi: 10.1109/INFCOMW.2017.8116527. Crossref
B. Nayak, P. S. Mugali, B. R. Rao, S. Sindhava, D. N. Disha, and K. S. Swarnalatha, “Geofencing-based accident avoidance notification for road safety,” Emerg. Res. Comput. Information, Commun. Appl., 2019, vol. 906, pp. 379–386. doi: 10.1007/978-981-13-6001-5_30. Crossref
F. Besoain, A. Perez-Navarro, C. J. Aviñó, J. A. Caylà, N. A. Barriga, and P. G. de Olalla, “Prevention of HIV and other sexually transmitted infections by geofencing and contextualized messages with a gamified app, UBESAFE: Design and creation study,” J. Medical Internet Res. Mhealth Uhealth, vol. 8, no. 3, 2020, Art. no. e1456, doi: 10.2196/14568. Crossref
S. Suryadi, E. Kurniawan, H. Adinanta, B. H. Sirenden, J. A. Prakosa, and P. Purwowibowo, “On the comparison of social distancing violation detectors with graphical processing unit support,” 2020 Int. Conf. Radar, Antenna, Microwave, Electron. Telecommun., 2020, pp. 337–342, doi: 10.1109/ICRAMET51080.2020.9298574. Crossref
M. Sharma, “Open-CV social distancing intelligent system,” 2020 2nd Int. Conf. Adv. Comput. Commun. Control Networking, 2020, pp. 972–975, doi: 10.1109/ICACCCN51052.2020.9362920. Crossref
P. Somaldo, F. A. Ferdiansyah, G. Jati, and W. Jatmiko, “Developing smart COVID-19 social distancing surveillance drone using YOLO implemented in robot operating system simulation environment,” 2020 IEEE 8th R10 Humanitarian Technol. Conf., 2020, doi: 10.1109/R10-HTC49770.2020.9357040. Crossref
A. Gad, G. Elbary, M. Alkhedher, and M. Ghazal, “Vision-based approach for automated social distance violators detection,” 2020 Int. Conf. Innov. Intell. Informatics Comput. Technol., 2020, doi: 10.1109/3ICT51146.2020.9311969. Crossref
F. A. A. Naqiyuddin, W. Mansor, N. M. Sallehuddin, M. N. S. M. Johari, M. A. S. Shazlan, and A. N. Bakar, “Wearable social distancing detection system,” 2020 IEEE Int. RF Microw. Conf., 2020, doi: 10.1109/RFM50841.2020.9344786. Crossref
“Traccar - modern GPS tracking platform.” https://www.traccar.org/ (accessed Dec. 28, 2021).
“Trial Methodologies.” https://github.com/opentrace-community/opentrace-calibration/blob/master/Trial Methodologies.md (accessed Dec. 28, 2021).
Z. Ozdemir and B. Tugrul, “Geofencing on the real-time GPS tracking system and improving GPS accuracy with moving average, Kalman filter and logistic regression analysis,” 2019 3rd Int. Symp. Multidiscip. Stud. Innov. Technol., 2019, doi: 10.1109/ISMSIT.2019.8932766. Crossref
P. Chunhakam, P. Pummarin, P. Jeen-Im, P. Wardkien, P. Wisartpong, and K. Lertteerada, “GPS position predicting system by Kalman filter with velocity from OBD and direction from magnetometer,” 2021 9th Int. Electr. Eng. Congr., 2021, pp. 444–447, doi: 10.1109/iEECON51072.2021.9440239. Crossref
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