  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 short-range 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 (wall- or 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.
