论文标题
使用3D卷积神经网络在手术室中进行全自动手动卫生监测
Fully Automated Hand Hygiene Monitoring\\in Operating Room using 3D Convolutional Neural Network
论文作者
论文摘要
手卫生是预防医院获得感染（HAI）的最重要因素之一，这些因素通常是由医务人员与手术室（OR）中患者接触的医务人员传播的。手动卫生监测对于调查和减少OR内感染的爆发可能很重要。但是，由于或场景的视觉复杂性，很难开发出一种有效的手动卫生监测工具。卷积神经网（CNN）的视频理解的最新进展增加了对人类行为的识别和检测的应用。利用这一进展，我们提出了一种使用3D CNN的时空特征在饮酒中摩擦的手动摩擦作用或视频的全自动手动卫生监测工具。首先，检测并裁剪了麻醉师上半身的感兴趣区域（ROI）。将时间平滑过滤器应用于ROI。然后，将ROI分配给3D CNN，并分为两个类：摩擦或其他动作。我们观察到，来自动力学400的转移学习是有益的，并且光流在我们的数据集中无济于事。测试中的最终精度，精度，召回和F1得分分别为0.76、0.85、0.65和0.74。
Hand hygiene is one of the most significant factors in preventing hospital acquired infections (HAI) which often be transmitted by medical staffs in contact with patients in the operating room (OR). Hand hygiene monitoring could be important to investigate and reduce the outbreak of infections within the OR. However, an effective monitoring tool for hand hygiene compliance is difficult to develop due to the visual complexity of the OR scene. Recent progress in video understanding with convolutional neural net (CNN) has increased the application of recognition and detection of human actions. Leveraging this progress, we proposed a fully automated hand hygiene monitoring tool of the alcohol-based hand rubbing action of anesthesiologists on OR video using spatio-temporal features with 3D CNN. First, the region of interest (ROI) of anesthesiologists' upper body were detected and cropped. A temporal smoothing filter was applied to the ROIs. Then, the ROIs were given to a 3D CNN and classified into two classes: rubbing hands or other actions. We observed that a transfer learning from Kinetics-400 is beneficial and the optical flow stream was not helpful in our dataset. The final accuracy, precision, recall and F1 score in testing is 0.76, 0.85, 0.65 and 0.74, respectively.
