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This paper presents a comparison of various deep learning models such asException, Resnet-50, and Inception V3 for the classification and prediction ofhand hygiene gestures, which were recorded in accordance with the World HealthOrganization (WHO) guidelines. The dataset consists of six hand hygienemovements in a video format, gathered for 30 participants. The network consistsof 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) isachieved in the classification report after the training of the models for 25epochs. ResNet-50 model clearly outperforms with correct class predictions. Themajor speed limitation can be overcome with the use of fast processing GPU forfuture work. A complete hand hygiene dataset along with other generic gesturessuch as one-hand movements (linear hand motion; circular hand rotation) will betested with ResNet-50 architecture and the variants for health care workers.

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