Instrumentation & Measurement Magazine 24-6 - 10

Fig. 5. The classification output of a postural sway monitoring system. Meaning of the JS
index: 0=Stable condition; 1=Unstable condition. b) The reliability index
estimated for each classified event as the relative distance between score values and the pre-defined threshold [18] (©2020 IEEE).
processing features related to COP, as previously discussed.
The figure shows predicted and estimated values of the score
for the whole set of events considered. As it can be observed,
the adopted classification strategy performs very well, and all
considered patterns have been correctly classified. Moreover,
a reliable index has been estimated, according to the above approach
of weighting relative distances between score values
and the pre-defined threshold. Results obtained for such index,
reported in Fig. 5b, confirm the suitability of the adopted
approach.
Assessment of AT Solutions
The assessment of solutions developed in the framework of AT
is mandatory for the realization of effective devices. To such aim,
laboratory assessment is required in order to test the technical
functionality of the developed systems. Moreover, test their usability
and acceptability, dedicated experimental surveys need
to be performed by involving end-users. Of course, users must
be involved also during the design and preliminary assessment
of AT, by following the user-centred design approach.
The assessment of AT requires also the definition of suitable
performance indexes, among which the following are the
most adopted:
Sensitivity Se 
:
Specificity Sp 
:
Accuracy Ac 
:
TP TN FP FN
 
As an example, with reference to results shown in Fig. 4,
mean values of Sensitivity and Specificity are 0.8 and 0.9, respectively
[17].
10
TP FN
TP

TN FP
TN

TP TN

(3)
(4)
(5)
Another particular aspect for the assessment of the adopted
classification strategy is the validation of its behavior against
the set of patterns adopted to estimate the classifier and the one
used to test its efficiency. An effective solution to handle this
need is the use of the K-Fold cross-validation approach. This is
an extension of the traditional cross-validation method, which
is used to estimate the skill of a model to generalize to an independent
dataset. Actually, the aim of cross-validation is to
test the generic model's ability to perform suitable prediction,
without suffering for overfitting or selection bias. The idea is
based on partitioning the dataset into complementary subsets,
performing the analysis on one subset (the training set) and
validating the analysis on the other subset (the testing set). The
extension of cross-validation to K different dataset partitions is
called K-fold cross-validation. In order to provide a synthetic
index, it is quite common to average assessment indexes estimated
through the K different pattern combinations [28].
Conclusions
This article addresses the challenging topic of AT and its role
in the improvement of life quality for the elderly and people
with impairments. In particular, most of the aspects to be considered
while designing and developing AT solutions have
been highlighted, with specific regards to fall detection and
postural instability, as well as ADLs. Actually, the availability
of " robust " solutions might really help end-users to improve
their safety, self-esteem and autonomy, as well as caregivers to
achieve real time information on end-users' status or needs.
Through this paper, the main components of adopted strategies
have been discussed, from pre-processing paradigms
to feature generation, and the approaches to build scores and
classification methods. A specific focus has been given to the
assessment procedure aimed to qualify the behavior of AT and
the strategic role of reliability indexes. The latter are mandatory,
as demonstrated in the case of classification paradigms.
IEEE Instrumentation & Measurement Magazine
September 2021

Instrumentation & Measurement Magazine 24-6

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