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.
翻译:洗手是预防传染病的最重要方法之一,包括COVID-19。 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(卫生组织)的洗手准则。为此目的,我们提供神经网络,自动识别卫生组织定义的不同洗手运动。我们用一个大型(2000+视频)真实世界标签数据集与不同的洗涤运动一起对神经网络进行培训。初步结果显示,使用预先训练的神经网络模型,例如用于这项任务的移动网络2和Xception,在确认不同的洗手运动时,可能达到>64%的精确度。我们还描述了作为这项工作一部分的上述开放存取数据集的收集和结构。最后,我们描述了如何利用神经网络来建立一个移动电话应用程序,用于自动质量控制和医疗专业人员实时反馈。