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首页> 外文会议>SPIE Medical Imaging Conference >Hand hygiene monitoring based on segmentation of interacting hands with convolutional networks
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Hand hygiene monitoring based on segmentation of interacting hands with convolutional networks

机译：基于卷积网络交互手的分割的手部卫生监控

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The number of health-care associated infections is increasing worldwide. Hand hygiene has been identified as one of the most crucial measures to prevent bacteria from spreading. However, compliance with recommended procedures for hand hygiene is generally poor, even in modern, industrialized regions. We present an optical assistance system for monitoring the hygienic hand disinfection procedure which is based on machine learning. Firstly, each hand and underarm of a person is detected in a down-sampled 96 px x 96 px depth video stream by pixelwise classification using a fully convolutional network. To gather the required amount of training data, we present a novel approach in automatically labeling recorded data using colored gloves and a color video stream that is registered to the depth stream. The colored gloves are used to segment the depth data in the training phase. During inference, the colored gloves are not required. The system detects and separates detailed hand parts of interacting, self-occluded hands within the observation zone of the sensor. Based on the location of the segmented hands, a full resolution region of interest (ROI) is cropped. A second deep neural network classifies the ROI into ten separate process steps (gestures), with nine of them based on the recommended hand disinfection procedure of the World Health Organization, and an additional error class. The combined system is cross-validated with 21 subjects and predicts with an accuracy of 93.37% (± 2.67%) which gesture is currently executed. The feedback is provided with 30 frames per second.
机译：保健相关感染的数量正在全世界增加。手工卫生已被确定为预防细菌扩散的最重要措施之一。但是，遵守建议的手工卫生程序通常是穷人，即使在现代工业化地区也是如此。我们介绍了一种用于监测基于机器学习的卫生手消毒程序的光学辅助系统。首先，使用完全卷积网络在下拍摄的96px x 96 px深度视频流中检测人的每只手和腋下。要收集所需的培训数据量，我们介绍了一种使用彩色手套和注册到深度流的彩色视频流自动标记录制数据的新方法。彩色手套用于分割训练阶段的深度数据。在推动期间，不需要彩色手套。系统检测并分离在传感器的观察区内的相互作用，自闭合手的详细的手部。基于分割手的位置，裁剪了一个全分辨率的感兴趣区域（ROI）。第二个深神经网络将ROI分类为十个独立的过程步骤（手势），基于世界健康组织的推荐的手消毒程序，以及额外的错误类别。组合系统与21个受试者交叉验证，并以93.37％（±2.67％）的准确性预测，该手势当前正在执行。反馈配有30帧每秒30帧。

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