Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines
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Date
2021-04-07
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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.
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Funding Information: Funding: This research was funded by the Ministry of Education and Science, Republic of Latvia, project “Integration of reliable technologies for protection against COVID-19 in healthcare and high-risk areas”, project No. VPP-COVID-2020/1-0004. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
hand-washing, hands, hand movement, video dataset, neural network, 1.2 Computer and information sciences, 2.6 Medical engineering, 3.3 Health sciences, 1.1. Scientific article indexed in Web of Science and/or Scopus database, SDG 3 - Good Health and Well-being
Citation
Lulla, M, Rutkovskis, A, Slavinska, A, Vilde, A, Gromova, A, Ivanovs, M, Skadins, A, Kadikis, R & Elsts, A 2021, 'Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines', Data, vol. 6, no. 4, 38. https://doi.org/10.3390/data6040038