Processing of Body-Induced Thermal Signatures for Physical Distancing and Temperature Screening

Abstract

Massive and unobtrusive screening of people in public environments is becoming a critical task to guarantee safety in congested shared spaces, as well as to support early non-invasive diagnosis and response to disease outbreaks. Among various sensors and Internet of Things (IoT) technologies, thermal vision systems, based on low-cost infrared (IR) array sensors, allow to track thermal signatures induced by moving people. Unlike contact tracing applications that exploit shortrange communications, IR-based sensing systems are passive, as they do not need the cooperation of the subject(s) and do not pose a threat to user privacy. The paper develops a signal processing framework that enables the joint analysis of subject mobility while automating the temperature screening process. The system consists of IR-based sensors that monitor both subject motions and health status through temperature measurements. Sensors are networked via wireless IoT tools and are deployed according to different configurations (wallor ceiling-mounted setups). The system targets the joint passive localization of subjects by tracking their mutual distance and direction of arrival, in addition to the detection of anomalous body temperatures for subjects close to the IR sensors. Focusing on Bayesian methods, the paper also addresses best practices and relevant implementation challenges using on field measurements. The proposed framework is privacy-neutral, it can be employed in public and private services for healthcare, smart living and shared spaces scenarios without any privacy concerns. Different configurations are also considered targeting both industrial, smart space and living environments.

Wireless Technologies for Social Distancing in the Time of COVID-19: Literature Review, Open Issues, and Limitations

“…The study [ 28 ] looked at how to interpret body-induced thermal signatures for physical distancing and temperature screening. A statistical model was used for the proposed framework to capture body-induced thermal signatures from noisy data, and a mobility model is used to detect multi-body activities and minimize erroneous target detection.…”

“…On one hand some studies have made their implementation using real experiments such as [ 27 , 30 , 35 , 36 , 39 , 40 , 41 ], while some studies have conducted a simulation-based implementations, such as [ 29 , 31 , 32 , 33 , 34 , 42 , 43 , 44 , 45 ]. On the other hand, few studies made both implementation, simulation based and real experiments, such as [ 28 , 37 ], while the study [ 38 ] did not present implementation method.…”

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

“…The authors of [10] developed a Bayesian framework to measure the body temperature of multiple users using low-cost passive infrared sensors. The distance from the sensors and the number of subjects is also obtained.…”

“…Denoting by B k the 2-D region of the image enclosed by the bounding box, and by B ki the intensity of its pixel i, it holds Tk = max i B ki . In line with [10], the direct reading Tk is subject to a scaling factor, α(d k ), with respect to the true subject's temperature T , where α(d k ) depends on the distance from the TC, i.e.,…”

“…This makes it possible to take temperature measurements from a subject and reliably associate them with the highly precise tracking of his/her movement performed by the radar. In addition, the joint analysis of radar and TC data allows refining the temperature estimated through the TC: to mitigate the influence of the distance on the temperature readings [10], a regression function that provides temperature correction coefficients is fit from training data. The final temperatures are obtained using such function with the accurate distances retrieved from the radar.…”