Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines

Martins Lulla, Aleksejs Rutkovskis, Andreta Slavinska, Aija Vilde, Anastasija Gromova, Maksims Ivanovs, Ansis Skadins, Roberts Kadikis and Atis Elsts
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Martins Lulla: Medical Education Technology Centre, Riga Stradins University, Dzirciema iela 16, LV-1007 Riga, Latvia
Aleksejs Rutkovskis: Medical Education Technology Centre, Riga Stradins University, Dzirciema iela 16, LV-1007 Riga, Latvia
Andreta Slavinska: Medical Education Technology Centre, Riga Stradins University, Dzirciema iela 16, LV-1007 Riga, Latvia
Aija Vilde: Medical Education Technology Centre, Riga Stradins University, Dzirciema iela 16, LV-1007 Riga, Latvia
Anastasija Gromova: Medical Education Technology Centre, Riga Stradins University, Dzirciema iela 16, LV-1007 Riga, Latvia
Maksims Ivanovs: Institute of Electronics and Computer Science (EDI), Dzerbenes 14, LV-1006 Riga, Latvia
Ansis Skadins: Institute of Electronics and Computer Science (EDI), Dzerbenes 14, LV-1006 Riga, Latvia
Roberts Kadikis: Institute of Electronics and Computer Science (EDI), Dzerbenes 14, LV-1006 Riga, Latvia
Atis Elsts: Medical Education Technology Centre, Riga Stradins University, Dzirciema iela 16, LV-1007 Riga, Latvia

Data, 2021, vol. 6, issue 4, 1-6

Abstract: Washing hands is one of the most important ways to prevent infectious diseases, including COVID-19. The World Health Organization (WHO) has published hand-washing guidelines. This paper presents a large real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Pauls Stradins Clinical University Hospital. There are 3185 hand-washing episodes in total, each of which is annotated by up to seven different persons. The annotations classify the washing movements according to the WHO guidelines by marking each frame in each video with a certain movement code. The intention of this “in-the-wild” dataset is two-fold: to serve as a basis for training machine-learning classifiers for automated hand-washing movement recognition and quality control, and to allow to investigation of the real-world quality of washing performed by working medical staff. We demonstrate how the data can be used to train a machine-learning classifier that achieves classification accuracy of 0.7511 on a test dataset.

Keywords: hand-washing; hand movements; video dataset (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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