In recent years, the number of Internet of Things (IoT) devices has increased rapidly. The Internet of Biometric Things (IoBT) can process biometrics and health signals, and it will greatly extend the range of biometric applications. The analysis of health signals in the IoBT can use computer-aided diagnosis techniques. However, most of the existing computer-aided diagnosis methods are developed for common diseases and are not suitable for rare diseases. Zero shot learning is a potential method for the computer-aided diagnosis of rare diseases because it can identify objects of unknown categories. However, the existing zero shot learning methods are based on attribute learning and rely on an attribute dataset. There is no attribute dataset for health signal processing. Therefore, the existing zero shot learning methods are not suitable for health signal processing. Based on the above background, we propose a zero shot augmentation learning model (ZSAL) in the IoBT for health signal processing. First, an expert doctor identifies the contour of a lesion and selects a background image without a lesion. Second, the computer automatically generates virtual images using zero shot augmentation technology. Finally, the generated virtual dataset is used to train a convolutional classifier, and then we apply the classifier to the computer-aided diagnosis of actual medical images. The experiment shows the efficiency and effectiveness of our method.

近年来，物联网（IoT）设备的数量迅速增加。生物特征物联网（IoBT）可以处理生物特征和健康信号，它将大大扩展生物特征的应用范围。IoBT中的健康信号分析可以使用计算机辅助诊断技术。但是，大多数现有的计算机辅助诊断方法都是针对常见疾病开发的，不适用于稀有疾病。零镜头学习是一种用于计算机辅助诊断罕见病的潜在方法，因为它可以识别未知类别的物体。然而，现有的零镜头学习方法是基于属性学习并且依赖于属性数据集。没有用于健康信号处理的属性数据集。所以，现有的零击学习方法不适用于健康信号处理。基于以上背景，我们在IoBT中提出了一种用于健康信号处理的零镜头增强学习模型（ZSAL）。首先，专家医生确定病变的轮廓并选择无病变的背景图像。其次，计算机使用零镜头增强技术自动生成虚拟图像。最后，将生成的虚拟数据集用于训练卷积分类器，然后将分类器应用于实际医学图像的计算机辅助诊断。实验表明了该方法的有效性和有效性。专家医生会识别出病变的轮廓，并选择没有病变的背景图像。其次，计算机使用零镜头增强技术自动生成虚拟图像。最后，将生成的虚拟数据集用于训练卷积分类器，然后将分类器应用于实际医学图像的计算机辅助诊断。实验表明了该方法的有效性和有效性。专家医生会识别出病变的轮廓，并选择没有病变的背景图像。其次，计算机使用零镜头增强技术自动生成虚拟图像。最后，将生成的虚拟数据集用于训练卷积分类器，然后将分类器应用于实际医学图像的计算机辅助诊断。实验表明了该方法的有效性和有效性。