Washing hands is one of the most important ways to prevent infectious diseases, including COVID-19. Unfortunately, medical staff does not always follow the World Health Organization (WHO) hand washing guidelines in their everyday work. To this end, we present neural networks for automatically recognizing the different washing movements defined by the WHO. We train the neural network on a part of a large (2000+ videos) real-world labeled dataset with the different washing movements. The preliminary results show that using pre-trained neural network models such as MobileNetV2 and Xception for the task, it is possible to achieve >64 % accuracy in recognizing the different washing movements. We also describe the collection and the structure of the above open-access dataset created as part of this work. Finally, we describe how the neural network can be used to construct a mobile phone application for automatic quality control and real-time feedback for medical professionals.

中文翻译：

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使用深度学习的自动洗手质量评估

洗手是预防包括COVID-19在内的传染病的最重要方法之一。不幸的是，医务人员在日常工作中并不总是遵循世界卫生组织（WHO）洗手准则。为此，我们提出了用于自动识别WHO定义的不同洗涤运动的神经网络。我们在带有不同洗涤动作的大型（2000多个视频）现实世界标记数据集的一部分上训练神经网络。初步结果表明，使用预训练的神经网络模型（例如MobileNetV2和Xception）来完成该任务，在识别不同的洗涤动作时可以实现> 64％的精度。我们还描述了作为这项工作的一部分而创建的上述开放访问数据集的集合和结构。最后，