Instrumentation & Measurement Magazine 24-6 - 60

Table 1 - SLEs' functionalities in supporting the management of an emergency like COVID-19
Situation of pandemic emergency
How SLEs can support
Importance of remote monitoring of the epidemic contagion
considering the analysis of big data in the measurement
process.
Relevance of monitoring of people in quarantine, also
when symptoms are not so evident. Contacts between the
healthcare providers and these people should be avoided
to limit contagion risk.
Prohibition of going outside without good justification
(e.g., work reason). Hospitals should be avoided, as
well as congregations of people (especially if chronic or
immunocompromised) in medical waiting rooms. Medical
home visits are not recommended.
Many visits and treatments are postponed, with the risk of
delaying early diagnosis of a disease or decreasing the
quality of therapy, thus making treatment and care more
difficult and less effective.
High risk of loneliness and social isolation, which can worsen
the well-being status of fragile people. Rural/remote areas
in particular should have the same opportunities as others.
information quality will lead to wrong decisions (GarbageIn-Garbage-Out
principle, GIGO). Metrology can surely help
in this field, acting as " the science of information quality, " to
support the identification of different sources of uncertainties
at different levels of the D3
AI algorithms represent a paramount approach in this
scenario, together with big data analysis and cloud
computing [8].
Healthcare providers can access the data measured within
the SLE to know the health, mental, and social status of
people in quarantine, without any risks of contagion,
making early diagnosis possible.
Tailored coaching services are identified to improve the QoL
of at-home people, who are able to remotely connect with
healthcare providers in case of need. The measurement
of emotion recognition and well-being can provide more
tailored services.
Data from the SLE are analyzed through AI algorithms
to promote early diagnosis and prompt clinicians'
intervention.
SLEs can tackle social isolation, helping ageing and fragile
people to stay connected with their family, friends, and
communities of interest. Equal access and quality of care
are guaranteed through remote monitoring.
M process, namely measuring,
processing, and decision uncertainties [9]. Numerical simulation,
such as the Monte Carlo method, can help to estimate
the measurement uncertainty of the whole system (expressed
as reported in the Guide to the Expression of Uncertainty in
Measurement - GUM) and determine how input uncertainties
propagate through the measurement chain, until arriving to
the final output of decision-making process.
The smart environment is also endorsed by other smart city
technologies, such as smart logistics (e.g., automatic vehicles
to properly distribute food and medicines, or robots to reduce
contacts with infected people in a hospital during routine patient
assessment or to perform diagnostic testing [10]), smart
education, smart infrastructure, etc. [11]. Wearable sensors obviously
play a pivotal role in these situations, providing the
ability to measure (and transfer) vital parameters of interest
(e.g., HR, body temperature) [12].
Different types of measurement devices can be deployed
in a SLE to configure a proper monitoring system of both environment
and the dweller [13], [14]:
◗ Physiological sensors, depicting the person's health
status;
◗ Environmental sensors, to measure environment characteristics,
parameters related to energy efficiency and
comfort perception and detect certain behaviors/activities
performed by the inhabitant;
◗ Wearable devices, to continuously monitor certain physiological
parameters or the level of activity;
◗ Smart objects, to help the tracking of the activities
performed by the subject.
Fig. 2. Smart Living Environment measurement process.
60
IEEE Instrumentation & Measurement Magazine
September 2021

Instrumentation & Measurement Magazine 24-6

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