# Automated Quality Assessment of Hand Washing Using Deep Learning

arXiv: https://arxiv.org/abs/2011.11383 · HTML: https://mlsanity.com/paper/2011.11383/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Multi-modal Atmospheric Sensing to Augment Wearable IMU-Based Hand Washing Detection](https://mlsanity.com/paper/2410.03549/) (2024)
- [WHO-Hand Hygiene Gesture Classification System](https://mlsanity.com/paper/2110.02842/) (2021)
- [Hand Hygiene Assessment via Joint Step Segmentation and Key Action Scorer](https://mlsanity.com/paper/2209.12221/) (2022)
- [Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene](https://mlsanity.com/paper/2109.02917/) (2021)
- [Hand Hygiene Video Classification Based on Deep Learning](https://mlsanity.com/paper/2108.08127/) (2021)
- [A Comparison of Deep Learning Models for the Prediction of Hand Hygiene Videos](https://mlsanity.com/paper/2111.02322/) (2021)

## Abstract (arXiv)

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.