  Hand hygiene is crucial for preventing viruses and infections. Due to the
pervasive outbreak of COVID-19, wearing a mask and hand hygiene appear to be
the most effective ways for the public to curb the spread of these viruses. The
World Health Organization (WHO) recommends a guideline for alcohol-based hand
rub in eight steps to ensure that all surfaces of hands are entirely clean. As
these steps involve complex gestures, human assessment of them lacks enough
accuracy. However, Deep Neural Network (DNN) and machine vision have made it
possible to accurately evaluate hand rubbing quality for the purposes of
training and feedback. In this paper, an automated deep learning based hand rub
assessment system with real-time feedback is presented. The system evaluates
the compliance with the 8-step guideline using a DNN architecture trained on a
dataset of videos collected from volunteers with various skin tones and hand
characteristics following the hand rubbing guideline. Various DNN architectures
were tested, and an Inception-ResNet model led to the best results with 97%
test accuracy. In the proposed system, an NVIDIA Jetson AGX Xavier embedded
board runs the software. The efficacy of the system is evaluated in a concrete
situation of being used by various users, and challenging steps are identified.
In this experiment, the average time taken by the hand rubbing steps among
volunteers is 27.2 seconds, which conforms to the WHO guidelines.
