CNN for Hand Washing Movement Classification: What Matters More - the Approach or the Dataset?

Good hand hygiene is one of the key factors in preventing infectious diseases, including COVID-19. Advances in machine learning have enabled automated hand hygiene evaluation, with research papers reporting highly accurate hand washing movement classification from video data. However, existing studies typically use datasets collected in lab conditions. In this paper, we apply state-of-the-art techniques such as MobileNetV2 based CNN, including two-stream and recurrent CNN, to three different datasets: a good-quality and uniform lab-based dataset, a more diverse lab-based dataset, and a large-scale real-life dataset collected in a hospital. The results show that while many of the approaches show good accuracy on the first dataset, the accuracy drops significantly o n t he m ore complex datasets. Moreover, all approaches fail to generalize on the third dataset, and only show slightly-better-than random accuracy on videos held out from the training set. This suggests that despite the high accuracy routinely reported in the research literature, the transition to real-world applications for hand washing quality monitoring is not going to be straightforward. © 2022 IEEE.

Hand-washing video dataset annotated according to the world health organization’s hand-washing guidelines

Washing hands is one of the most important ways to prevent infectious diseases, including COVID-19. The World Health Organization (WHO) has published hand-washing guidelines. This paper presents a large real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Pauls Stradins Clinical University Hospital. There are 3185 hand-washing episodes in total, each of which is annotated by up to seven different persons. The annotations classify the washing movements according to the WHO guidelines by marking each frame in each video with a certain movement code. The intention of this “in-the-wild” dataset is two-fold: to serve as a basis for training machine-learning classifiers for automated hand-washing movement recognition and quality control, and to allow to investigation of the real-world quality of washing performed by working medical staff. We demonstrate how the data can be used to train a machine-learning classifier that achieves classification accuracy of 0.7511 on a test dataset. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.