{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T23:16:05Z","timestamp":1763507765926,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000181","name":"United States Air Force Office of Scientific Research","doi-asserted-by":"publisher","award":["FA2386-20-1-4045"],"award-info":[{"award-number":["FA2386-20-1-4045"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule. In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS). The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 \u00b0C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (kmodel) with an average 56 ms execution time in different networks, regardless of the network connection type or speed.<\/jats:p>","DOI":"10.3390\/s22010279","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T23:29:07Z","timestamp":1640906947000},"page":"279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Physical Distancing Device with Edge Computing for COVID-19 (PADDIE-C19)"],"prefix":"10.3390","volume":"22","author":[{"given":"Chun Hoe","family":"Loke","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2655-6800","authenticated-orcid":false,"given":"Mohammed Sani","family":"Adam","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9254-2023","authenticated-orcid":false,"given":"Rosdiadee","family":"Nordin","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6593-5603","authenticated-orcid":false,"given":"Nor Fadzilah","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8514-1459","authenticated-orcid":false,"given":"Asma","family":"Abu-Samah","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"ref_1","unstructured":"Law, T. (Time, 2021). 2 Million People Have Died From COVID-19 Worldwide, Time."},{"key":"ref_2","unstructured":"WHO (2021). Coronavirus Disease (COVID-19) Advice for the Public, WHO."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.ijid.2020.05.093","article-title":"COVID-19 outbreak in Malaysia: Actions taken by the Malaysian government","volume":"97","author":"Shah","year":"2020","journal-title":"Int. J. Infect. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Abdali, T.-A.N., Hassan, R., and Aman, A.H.M. (2021, January 29\u201331). A new feature in mysejahtera application to monitoring the spread of COVID-19 using fog computing. Proceedings of the 2021 3rd International Cyber Resilience Conference (CRC), Virtual Conference.","DOI":"10.1109\/CRC50527.2021.9392534"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Albayati, A., Abdullah, N.F., Abu-Samah, A., Mutlag, A.H., and Nordin, R. (2020). A Serverless Advanced Metering Infrastructure Based on Fog-Edge Computing for a Smart Grid: A Comparison Study for Energy Sector in Iraq. Energies, 13.","DOI":"10.3390\/en13205460"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75961","DOI":"10.1109\/ACCESS.2021.3081770","article-title":"Fog Computing Advancement: Concept, Architecture, Applications, Advantages, and Open Issues","volume":"9","author":"Abdali","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"159402","DOI":"10.1109\/ACCESS.2020.3020513","article-title":"Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model","volume":"8","author":"Garg","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5367","DOI":"10.1109\/JSYST.2021.3055675","article-title":"COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing","volume":"15","author":"Ng","year":"2021","journal-title":"IEEE Syst. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bian, S., Zhou, B., and Lukowicz, P. (2020). Social distance monitor with a wearable magnetic field proximity sensor. Sensors, 20.","DOI":"10.3390\/s20185101"},{"key":"ref_10","first-page":"204","article-title":"Novel economical social distancing smart device for covid19","volume":"11","author":"Nadikattu","year":"2020","journal-title":"Int. J. Electr. Eng. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sathyamoorthy, A.J., Patel, U., Savle, Y.A., Paul, M., and Manocha, D. (2020). COVID-Robot: Monitoring social distancing constraints in crowded scenarios. arXiv.","DOI":"10.1371\/journal.pone.0259713"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rezaei, M., and Azarmi, M. (2020). Deepsocial: Social distancing monitoring and infection risk assessment in covid-19 pandemic. Appl. Sci., 10.","DOI":"10.1101\/2020.08.27.20183277"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"153479","DOI":"10.1109\/ACCESS.2020.3018140","article-title":"A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing\u2014Part I: Fundamentals and Enabling Technologies","volume":"8","author":"Nguyen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1177\/1329878X20949770","article-title":"COVID-19 apps in Singapore and Australia: Reimagining healthy nations with digital technology","volume":"177","author":"Goggin","year":"2020","journal-title":"Media Int. Aust."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Azlan, A.A., Hamzah, M.R., Sern, T.J., Ayub, S.H., and Mohamad, E. (2020). Public knowledge, attitudes and practices towards COVID-19: A cross-sectional study in Malaysia. PLoS ONE, 15.","DOI":"10.1101\/2020.04.29.20085563"},{"key":"ref_16","unstructured":"Idris, M.N.M. (Utusan Malaysia, 2020). 606 Kompaun Langgar SOP di Selangor, Utusan Malaysia."},{"key":"ref_17","unstructured":"WHO (2020). COVID-19 Significantly Impacts Health Services for Noncommunicable Diseases, WHO."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mohsin, J., Saleh, F.H., and Al-muqarm, A.M.A. (2020, January 28\u201330). Real-time Surveillance System to detect and analyzers the Suspects of COVID-19 patients by using IoT under edge computing techniques (RS-SYS). Proceedings of the 2020 2nd Al-Noor International Conference for Science and Technology (NICST), Baku, Azerbaijan.","DOI":"10.1109\/NICST50904.2020.9280305"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MCE.2020.3031261","article-title":"Novel MEC based Approaches for Smart Hospitals to Combat COVID-19 Pandemic","volume":"10","author":"Ranaweera","year":"2020","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hegde, C., Jiang, Z., Suresha, P.B., Zelko, J., Seyedi, S., Smith, M., Wright, D., Kamaleswaran, R., Reyna, M., and Clifford, G. (2020). AutoTriage\u2014An Open Source Edge Computing Raspberry Pi-based Clinical Screening System. medRxiv, 1\u201313.","DOI":"10.1101\/2020.04.09.20059840"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s11554-021-01070-6","article-title":"Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19","volume":"18","author":"Saponara","year":"2021","journal-title":"J. Real-Time Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rahim, A., Maqbool, A., and Rana, T. (2021). Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247440"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1109\/ACCESS.2020.3045792","article-title":"Robots under COVID-19 Pandemic: A Comprehensive Survey","volume":"9","author":"Shen","year":"2021","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_25","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/279\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:56:21Z","timestamp":1760169381000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,30]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010279"],"URL":"https:\/\/doi.org\/10.3390\/s22010279","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,12,30]]}}}