  In the recent years there has been a growing interest in techniques able to
automatically recognize activities performed by people. This field is known as
Human Activity recognition (HAR). HAR can be crucial in monitoring the
wellbeing of the people, with special regard to the elder population and those
people affected by degenerative conditions. One of the main challenges concerns
the diversity of the population and how the same activities can be performed in
different ways due to physical characteristics and life-style. In this paper we
explore the possibility of exploiting physical characteristics and signal
similarity to achieve better results with respect to deep learning classifiers
that do not rely on this information.
