Instrumentation & Measurement Magazine 24-6 - 61

Fig. 3. Examples of sensors useful in SLEs.
Some useful sensors that can be deployed in a SLE are
reported in Fig. 3 and in Table 2, where the measured parameters,
the required measurement range, and the required
accuracy are also reported.
Therefore, it is possible to derive indices related to the wellbeing
of inhabitants, which are significant measures of their
physiological, mental, and social status and can be quantified
in an objective manner with a determined level of accuracy,
depending on the uncertainty of the sensors constituting
the network. This is important to adopt timely interventions
aimed at improving the dwellers' QoL.
Nowadays the choice of sensors is mainly directed to the
unobtrusiveness of the sensors and favors non-contact and
minimally invasive ones (i.e., smartwatch, which is not different
from a common watch). In fact, if proper non-invasive
sensors are used, the inhabitants can perform daily activities
as usual, without the influence of the measurement devices
employed in the SLE where they live. This is important, for
example, in the case of emotion recognition: if cameras were
used, users could be reluctant to freely give space to emotions,
and, on the contrary, they would be induced to hide them (and
privacy issues would inevitably arise). On the other hand, if
no sensors hinder activities in the environment, everything is
more adherent to natural behavior. Also, the assessment and
the recognition of the Activities of Daily Living (ADLs) can
September 2021
be useful to evaluate the well-being and identify eventual abnormalities
possibly linked to some health issues. Wearable
technologies play an important role in this field, but, despite
their rapid diffusion, it is necessary to take into account their
measurement properties [15], obviously influencing the output
(e.g., in terms of ADL classification accuracy [16]). All these
aspects related to sensors should be included in a certification
scheme for AFH, to define a sort of checklist for those who design
these environments, such as architects and engineers.
Measurement of Personal Well-being in
a SLE: A Step Further Thanks to the AI
Approach
The measurement of well-being indices can be extremely useful
to classify a person's physical, mental, and social status.
To this aim, the sensors deployed within the SLE are fundamental
to provide big data determining these parameters; AI
can provide a huge contribution in this analysis, not limiting
the processing to single sensor data but properly fusing the
information from different sensors and considering the different
weights attributed to the various parameters in a specific
training phase. This allows suitable actions to be tailored to improve
the person's QoL.
In the training phase, it is important to provide the AI algorithms
significant information about the parameters mostly
IEEE Instrumentation & Measurement Magazine
61

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