  Washing hands is one of the most important ways to prevent infectious
diseases, including COVID-19. Unfortunately, medical staff does not always
follow the World Health Organization (WHO) hand washing guidelines in their
everyday work. To this end, we present neural networks for automatically
recognizing the different washing movements defined by the WHO. We train the
neural network on a part of a large (2000+ videos) real-world labeled dataset
with the different washing movements. The preliminary results show that using
pre-trained neural network models such as MobileNetV2 and Xception for the
task, it is possible to achieve >64 % accuracy in recognizing the different
washing movements. We also describe the collection and the structure of the
above open-access dataset created as part of this work. Finally, we describe
how the neural network can be used to construct a mobile phone application for
automatic quality control and real-time feedback for medical professionals.

Human-Computer Interaction;Computer Vision and Pattern Recognition;Machine Learning