WHO-Hand Hygiene Gesture Classification System

TL;DR

This study develops a machine learning system using ResNet50 to classify WHO hand hygiene gestures, achieving up to 72% accuracy, aiming to improve hand hygiene compliance in healthcare settings.

Contribution

The paper introduces a novel dataset and applies transfer learning with ResNet50 for classifying WHO hand hygiene gestures, advancing automated hand hygiene monitoring.

Findings

72% accuracy on second gesture set

Transfer learning effectively classifies hand hygiene gestures

Preliminary dataset enables future real-time deployment

Abstract

The recent ongoing coronavirus pandemic highlights the importance of hand hygiene practices in our daily lives, with governments and worldwide health authorities promoting good hand hygiene practices. More than one million cases of hospital-acquired infections occur in Europe annually. Hand hygiene compliance may reduce the risk of transmission by reducing the number of infections as well as healthcare expenditures. In this paper, the World Health Organization, hand hygiene gestures are recorded and analyzed with the construction of an aluminum frame, placed at the laboratory sink. The hand hygiene gestures are recorded for thirty participants after conducting a training session about hand hygiene gestures demonstration. The video recordings are converted into image files and are organized into six different hand hygiene classes. The Resnet50 framework selection for the classification of multiclass hand hygiene stages. The model is trained with the first set of classes; Fingers Interlaced, P2PFingers Interlaced, and Rotational Rub for 25 epochs. An accuracy of 44 percent for the first set of experiments with a loss score greater than 1.5 in the validation set is achieved. The training steps for the second set of classes; Rub hands palm to palm, Fingers Interlocked, Thumb Rub are 50 epochs. An accuracy of 72 percent is achieved for the second set with a loss score of less than 0.8 for the validation set. In this work, a preliminary analysis of a robust hand hygiene dataset with transfer learning takes place. The future aim for deploying a hand hygiene prediction system for healthcare workers in real-time.