Instrumentation & Measurement Magazine 24-6 - 7

In order to build a reliable
set of signatures (one
per each addressed class
of events), a high-quality
dataset that includes several
observations for each
class is required. The signatures
building process, well
described in [16], often requires
features alignment
and averaging. As an example,
Fig. 2 shows signatures
obtained for traditional
classes of Falls and ADLs:
Forward Fall (FF), Lateral
Fall (LF), Backward Fall
(BF), Step Up/Down by
the Right/Left leg, 'SUR,L
SDR,L
',
', Lying Down (LD)
and SItting (SI) [16].
The evaluation of how
close an event is to a given
signature is demanded
of an operator measuring
their similarity, such
as Cross-Correlation [25]
or Dynamic Time Warping
(DTW) technique [26].
Cross-Correlation is known
to be one of the most powerful,
yet computationally
manageable, methods for
similarity measurements,
especially thanks to the
robustness to exogenous
factor (e.g., noise). Through
this approach, one possible
definition of the score is
the maximum value of the
cross-correlation between
features obtained by the
unknown pattern and signatures
that represent the
different classes of events.
DTW, instead, is one of
Fig. 1. Schematization of classification strategies.
the algorithms for measuring similarity between two temporal
sequences, which may vary in speed or be sampled
differently. For instance, similarities in walking could be detected
using DTW, even if one person walks faster than the
other or in case accelerations and decelerations occur during
an observation. This also enables the possibility to use signatures
built on a dataset with different sampling frequencies,
rather than the one adopted during the real-time system operation.
The two temporal sequences to be investigated are
" warped " non-linearly to provide a measure of their similarity
which is robust against non-linear variations in time. The
September 2021
DTW algorithm intrinsically computes the " similarity " between
signatures (for all the considered classes) and features
of the unknown pattern, which represent scores to be used by
the classification algorithm.
The Classification Task
The classification task aims to provide an index to define the
chance that an unknown pattern belongs to a well-defined
class of events. The use of classification algorithms mainly
comparing the value of very basic features, such as the acceleration
amplitude, to heuristically defined thresholds is strongly
IEEE Instrumentation & Measurement Magazine
7

Instrumentation & Measurement Magazine 24-6

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