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

TL;DR

This study compares deep learning models like ResNet-50, Inception V3, and Xception for classifying hand hygiene gestures from videos, highlighting ResNet-50's superior accuracy and discussing future improvements with faster processing hardware.

Contribution

It evaluates and compares multiple deep learning architectures for hand hygiene gesture recognition, providing insights into their performance on a specific healthcare dataset.

Findings

ResNet-50 achieved 72% accuracy, outperforming other models.

Inception V3 achieved 33% accuracy, and Xception 37%.

ResNet-50's accuracy suggests it is most suitable for this task.

Abstract

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.