  Current guidelines from the World Health Organization indicate that the
SARS-CoV-2 coronavirus, which results in the novel coronavirus disease
(COVID-19), is transmitted through respiratory droplets or by contact. Contact
transmission occurs when contaminated hands touch the mucous membrane of the
mouth, nose, or eyes so hands hygiene is extremely important to prevent the
spread of the SARSCoV-2 as well as of other pathogens. The vast proliferation
of wearable devices, such as smartwatches, containing acceleration, rotation,
magnetic field sensors, etc., together with the modern technologies of
artificial intelligence, such as machine learning and more recently
deep-learning, allow the development of accurate applications for recognition
and classification of human activities such as: walking, climbing stairs,
running, clapping, sitting, sleeping, etc. In this work, we evaluate the
feasibility of a machine learning based system which, starting from inertial
signals collected from wearable devices such as current smartwatches,
recognizes when a subject is washing or rubbing its hands. Preliminary results,
obtained over two different datasets, show a classification accuracy of about
95% and of about 94% for respectively deep and standard learning techniques.
