Enabling and Emerging Technologies for Social Distancing: A Comprehensive Survey and Open Problems

Abstract

Social distancing is crucial for preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the closely physical contact between people, we can reduce chances of catching the virus and spreading it to the community. This paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, AI can enable, encourage, and even enforce social distancing. To that end, we provide a fundamental background of social distancing including basic concepts, measurements, models and propose practical scenarios. We then discuss enabling wireless technologies which are especially effective and can be widely adopted in practice to keep distance and monitor people. After that, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we provide important open issues and challenges (e.g., privacy-preserving, cybersecurity) in implementing social distancing in practice.

MilliTRACE-IR: Contact Tracing and Temperature Screening via mm-Wave and Infrared Sensing

“…Here, we discuss several prior works in related areas, highlighting the differences with respect to the system proposed in this paper. Social distancing monitoring: Social distancing has been one of the most widely employed countermeasures to contagious diseases outbreaks [1]. Real-time monitoring of the distance between people in workplaces or public buildings is key for risk assessment and to prevent the formation of crowds.…”

“…Existing approaches use either wireless technology like Bluetooth or WiFi [11], [12], which require the users to carry a mobile device, or camera-based systems [13], which are privacy invasive. Other approaches use the received signal strength indication (RSSI) from cellular communication protocols [1] or wearables [14], although these are often inaccurate, especially when used in crowded places [1]. A lot of effort has been put into designing person detection and tracking algorithms for crowd monitoring and people counting [15] by using fixed surveillance cameras and mobile robots [16].…”

“…For our results, we selected d th = 1.2 m and γ = 9.21. 1 Then, the labels assigned by DBSCAN to all the points belonging to a cluster whose centroid falls inside region R(G) = ∪ t∈G (R c (t) ∩ R s (t)), are discarded (lines 7 − 9 in Alg. 1).…”

“…We use the new labels to replace the ones previously found by DBSCAN (Fig. 2d), unless the GM clusters have very small weights, i.e., the new clusters 1 This value corresponds to a probability of 99% of falling inside the region, assuming that the points in the cluster are distributed on the plane according to a Gaussian distribution around ŝt k . 2 Discarding a label corresponds to setting it equal to that used by DBSCAN to represent noise points.…”

“…Lately, social distancing has become a primary strategy to counteract the COVID-19 infection. Many research works [1], [2] have shown that it is an effective non-pharmacological approach and an important inhibitor for limiting the transmission of many contagious diseases such as H1N1, SARS, and COVID-19. Along with social distancing, elevated skin temperature detection and contact tracing have proven to be key to effectively contain the pandemic [3].…”