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

Lulla, Martins; Rutkovskis, Aleksejs; Slavinska, Andreta; Vilde, Aija; Gromova, A V; Ivanovs, Maksims; Skadins, Ansis; Kadiķis, Roberts; Elsts, Atis

doi:10.3390/data6040038

Cited by 11 publications

(10 citation statements)

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“…Washing hands properly can help prevent the spread and eliminate viruses on the surface of the hands that emerge after coughing, sneezing, and touching objects containing infectious pathogen. Handwashing also reduces the rate of respiratory infections and prevent them to enter the body or transmit (20)(21)(22)(23).…”

Section: Discussionmentioning

confidence: 99%

Quality of Handwashing in Informal Workers in Indonesia

Rachmat

Kristanto

Sondari

2022

JKL

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Introduction: Worker behavior of handwashing with soap in the informal sector has rarely not been studied in Indonesia. This study was conducted to determine factors related to the quality of handwashing performed by workers in the formal and informal sectors in Indonesia. Methods: The research used secondary data from Indonesia Basic Health Research. The research design was cross-sectional, and a total sample included was 421,404 workers at the productive age of 15-64 years across 34 provinces in Indonesia. The data collected included age, gender, marital status, education, place of residence, occupation, and handwashing practice. The determinants were identified using binary logistic regression. Results and Discussion: Results showed that 1.9% of workers did not wash their hands, and 35.0% only washed their hands with water; most of them worked in the informal sector (77.5%). Regarding the characteristics of workers, age (OR=1.17; 95% CI: 1.14–1.21), gender (OR=1.23; 95% CI: 0.93–0.99), education (OR=2.07; 95% CI: 2.01–2.14), and formal workplace (OR=1.43; 95% CI: 1.40-1.46) were mostly related to the quality of handwashing. Conclusion: The government is expected to formulate a structured policy in educating the workers, especially for male, young (15-24 years), low educated, and informal workers, about good handwashing. This study recommends that the government should use the current research findings to target proper population for the policy implementation.

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Section: Discussionmentioning

confidence: 99%

Quality of Handwashing in Informal Workers in Indonesia

Rachmat

Kristanto

Sondari

2022

JKL

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“…Then they were used to evaluate the match between the annotations created by the human operator and the neural network. The neural network was trained on a dataset that included both real-life hand-washing episodes from a hospital [29] and some of the experiments. More specifically, 20% of the experiments were classified as test data for the neural network and held out from the training phase.…”

Section: Neural Networkmentioning

confidence: 99%

“…Subsequently, these test videos were used to test the accuracy of the neural network and its match with the human operator. The architecture of the neural network was the same as when [29], specifically, transfer learning on top of MobileNetV2 was used.…”

Section: Neural Networkmentioning

confidence: 99%

Influence of different types of real-time feedback on hand washing quality assessed with neural networks/simulated neural networks

et al. 2022

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Background: Thousands of people die every day around the world from infections acquired in a hospital. Hands are the main pathways of germ transmission during healthcare. Hand hygiene monitoring can be performed using various methods. One of the latest techniques that can combine all is a neural network-based hand hygiene monitoring system. Methods/Design: Each participant performed 3 hand-washing trials, each time receiving different type of feedback. The order in which each participant of the study used the developed applications was strictly defined, thus each hand-washing study session started with performing hand washing using application A, B and C accordingly. All captured videos of hand-wash episodes were saved and later analysed with neural networks. In the end, both evaluation results were compared and evaluated. Results show that when the participants use Application Type A, they perform hand washing much faster, as well as in comparison of Application Type A versus application type C. However, the longest time spent for the hand washing was detected while using the application type B. Conclusion: Study shows that structured guidance provided during the real time hand washing could be associated with better overall performance. The Application C has confirmed its effectiveness. Proving its advantage among other applications, the Application C can be integrated into the clinical environment

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“…Another large-scale dataset is introduced in Lulla et al (2021) . The authors collected and labeled 3185 video files on various backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Real-time hand rubbing quality estimation using deep learning enhanced by separation index and feature-based confidence metric

Haghpanah

Vali

Torkamani

et al. 2023

Expert Systems with Applications

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

Cited by 11 publications

References 10 publications

Quality of Handwashing in Informal Workers in Indonesia

Quality of Handwashing in Informal Workers in Indonesia

Influence of different types of real-time feedback on hand washing quality assessed with neural networks/simulated neural networks

Real-time hand rubbing quality estimation using deep learning enhanced by separation index and feature-based confidence metric

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