FAROOQUE HASSAN KUMBHAR, SYED ALİ HASSAN, SOO YOUNG SHİN

New normal: cooperative paradigm for COVID-19 timely detection and containment using Internet of things and deep learning

The spread of the novel coronavirus (COVID-19) has caused trillions of dollars of damages to the governments and health authorities by affecting the global economies. It is essential to identify, track and trace COVID-19 spread at its earliest detection. Timely action can not only reduce further spread but also help in providing an efficient medical response. Existing schemes rely on volunteer participation, and/or mobile traceability, which leads to delays in containing the spread. There is a need for an autonomous, connected, and centralized paradigm that can identify, trace and inform connected personals. We propose a novel connected Internet of Things (IoT) based paradigm using convolution neural networks (CNN), smart wearable, and connected E-Health to help governments resume normal life again. Our autonomous scheme provides three-level detection: inter-object distance for social distancing violations using CNN, area-based monitoring of active smartphone users and their current state of illness using connected E-Health, and smart wearable. Our exhaustive performance evaluation identifies that the proposed scheme with CNN YOLOv3 achieves up to 90% mean average precision in detecting social distancing violations, as compared to existing YOLOv2 achieving 70% and faster R-CNN with 76%. Our evaluation also identifies that wearing protective gear reduces spread by 50%, and timely actions in the first hour can help avoid three times COVID-19 exposure.

PDF

___

[1] Chamola V, Hassija V, Gupta V, Guizani M. A comprehensive review of the covid-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. IEEE Access 2020; 8: 90225-90265. doi: 10.1109/ACCESS.2020.2992341
[2] Hossain M, Muhammad G, Guizani N. Explainable AI and mass surveillance system-based healthcare framework to combat COVID-19 like pandemics. IEEE Network 2020; 34 (4): 126-132. doi: 10.1109/MNET.011.2000458
[3] Mobasheri M, Kim Y, Kim W. Toward developing fog decision making on the transmission rate of various IoT devices based on reinforcement learning. IEEE Internet Things Magazine 2020; 3 (1): 38-42. doi: 10.1109/IOTM.0001.1900070
[4] Awaisi K, Hussain S, Ahmed M, Khan A, Ahmed G. Leveraging IoT and fog computing in healthcare systems. IEEE Internet Things Magazine 2020; 3 (2): 52-56. doi: 10.1109/IOTM.0001.1900096
[5] Abbas R, Michael K. COVID-19 contact trace app deployments: Learning from Australia and Singapore. IEEE Consumer Electronics Magazine 2020; 9 (5): 65-70. doi: 10.1109/MCE.2020.3002490
6] Hernandez-Orallo E, Manzoni P, Calafate C, Cano J. Evaluating how smartphone contact tracing technology can reduce the spread of infectious diseases: The case of COVID-19. IEEE Access 2020; 8: 99083-99097. doi: 10.1109/ACCESS.2020.2998042
[7] Michael K, Abbas R. Behind COVID-19 contact trace apps: The Google-Apple partnership. IEEE Consumer Electronics Magazine 2020; 9 (5): 71-76. doi: 10.1109/MCE.2020.3002492
[8] Stojanovic R, Skraba A, Lutovac B. A headset like wearable device to track COVID-19 symptoms. In: 9th Mediterranean Conference on Embedded Computing (MECO); Budva, Montenegro; 2020. pp. 1-4. doi: 10.1109/MECO49872.2020.9134211
[9] Punn N, Sonbhadra S, Agarwal S. Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques. ArXiv:2005.01385 2020; 1-10.
[10] Joseph R, Farhadi A. YOLOv3: An incremental improvement. ArXiv:1804.02767 2018; 1-6.
[11] Alsaeedy A, Chong E. Detecting regions at risk for spreading COVID-19 using existing cellular wireless network functionalities. IEEE Open Journal of Engineering in Medicine and Biology 2020; 1: 187-189. doi: 10.1109/OJEMB.2020.3002447
[12] Syed A, Tariq R, Soo Y. S. Real-time UAV detection based on deep learning network. In: 2019 International Conference on Information and Communication Technology Convergence (ICTC)); Jeju, South Korea; 2019. pp. 630-632. doi: 10.1109/ICTC46691.2019.8939564
[13] ŞENTÜRK, Ü, Polat, K, YÜCEDAĞ İ. Towards wearable blood pressure measurement systems from biosignals: A review. Turkish Journal Of Electrical Engineering and Computer Sciences 2019; 27 (5): 3259-3281. doi: 10.3906/elk1812-121
[14] Mangla M, Sharma N, Mittal P. A fuzzy expert system for predicting the mortality of COVID’19. Turkish Journal Of Electrical Engineering and Computer Sciences 2021; 29 (3): 1628-1642. doi: 10.3906/elk-2008-27
[15] Özbay H, Dalcali, A. Effects of COVID-19 on electric energy consumption in Turkey and ANN-based short-term forecasting. Turkish Journal Of Electrical Engineering and Computer Sciences 2021; 29 (1): 78-97. doi: 10.3906/elk2006-29
[16] Acar E, Yilmaz I. COVID-19 detection on IBM quantum computer with classical-quantum transfer learning. Turkish Journal Of Electrical Engineering and Computer Sciences 2021; 29 (1): 46-61. doi: 10.3906/elk-2006-94
[17] Roy A, Kumbhar F. H, Dhillon H. S, Saxena N, Shin S. Y, Singh S. Efficient monitoring and contact tracing for covid-19: A smart IoT-based framework. IEEE Internet of Things Magazine 2020; 3 (3): 17-23. doi: 10.1109/IOTM.0001.2000145
[18] Antonini M, Vecchio M, Antonelli F. Fog computing architectures: A reference for practitioners. IEEE Internet Things Magazine 2019; 2 (3): 19-25. doi: 10.1109/IOTM.0001.1900029
[19] Guo C, Tian P, Choo K.K. R. Enabling privacy-assured fog-based data aggregation in E-healthcare systems. IEEE Transactions on Industrial Informatics 2021; 17 (3): 1948-1957. doi: 10.1109/TII.2020.2995228
[20] Fang W, Wang L, Ren P. Tinier-YOLO: A real-time object detection method for constrained environments. IEEE Access 2020; 8: 1935-1944. doi: 10.1109/ACCESS.2019.2961959
[21] Sahraoui Y, Kerrache CA, Korichi A, Nour B, Adnane A et al. DeepDist: A deep-learning-based ıov framework for real-time objects and distance violation detection. IEEE Internet of Things Magazine 2020; 3 (3): 30-34. doi: 10.1109/IOTM.0001.2000116