# A Comparison of Deep Learning Models for the Prediction of Hand Hygiene Videos

arXiv: https://arxiv.org/abs/2111.02322 · HTML: https://mlsanity.com/paper/2111.02322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Visually Indicated Sounds](https://mlsanity.com/paper/1512.08512/) (2015)
- [Reading Text in the Wild with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1842/) (2014)
- [Automated Quality Assessment of Hand Washing Using Deep Learning](https://mlsanity.com/paper/2011.11383/) (2020)
- [Feature Detection for Hand Hygiene Stages](https://mlsanity.com/paper/2108.03015/) (2021)
- [WHO-Hand Hygiene Gesture Classification System](https://mlsanity.com/paper/2110.02842/) (2021)

## Abstract (arXiv)

This paper presents a comparison of various deep learning models such as Exception, Resnet-50, and Inception V3 for the classification and prediction of hand hygiene gestures, which were recorded in accordance with the World Health Organization (WHO) guidelines. The dataset consists of six hand hygiene movements in a video format, gathered for 30 participants. The network consists of pre-trained models with image net weights and a modified head of the model. An accuracy of 37% (Xception model), 33% (Inception V3), and 72% (ResNet-50) is achieved in the classification report after the training of the models for 25 epochs. ResNet-50 model clearly outperforms with correct class predictions. The major speed limitation can be overcome with the use of fast processing GPU for future work. A complete hand hygiene dataset along with other generic gestures such as one-hand movements (linear hand motion; circular hand rotation) will be tested with ResNet-50 architecture and the variants for health care workers.