hear-your-action：基于超声波主动传感的人体动作识别
hear-your-action: human action recognition by ultrasound active sensing
AI总结：
本文提出一种基于超声波主动传感的隐私保护动作识别方法，构建新数据集并利用SVM和VGG对八类动作分类，在相同和不同用户下分别达97.9%和89.5%准确率。
摘要：
动作识别是许多工业应用的关键技术。使用图像等视觉信息的方法非常流行。然而，由于包含识别用户动作所不需要的私人信息（如可见的面部和场景背景），隐私问题阻碍了其广泛应用。本文提出了一种基于超声波主动传感的隐私保护动作识别方法。由于以非侵入方式进行超声波主动传感的动作识别尚未得到充分研究，我们创建了一个新的动作识别数据集，并对用于分类的特征进行了比较。我们通过关注超声波反射波振幅的时间变化来计算特征值，并使用支持向量机和VGG对八个基本动作类别进行了分类。我们证实，在同一人且同一环境下进行训练和评估时，该方法达到了97.9%的准确率。此外，即使在不同的人身上进行训练和评估，该方法也达到了89.5%的准确率。我们还报告了在各种条件下的准确性分析及局限性。
英文摘要：
Action recognition is a key technology for many industrial applications. Methods using visual information such as images are very popular. However, privacy issues prevent widespread usage due to the inclusion of private information, such as visible faces and scene backgrounds, which are not necessary for recognizing user action. In this paper, we propose a privacy-preserving action recognition by ultrasound active sensing. As action recognition from ultrasound active sensing in a non-invasive manner is not well investigated, we create a new dataset for action recognition and conduct a comparison of features for classification. We calculated feature values by focusing on the temporal variation of the amplitude of ultrasound reflected waves and performed classification using a support vector machine and VGG for eight fundamental action classes. We confirmed that our method achieved an accuracy of 97.9% when trained and evaluated on the same person and in the same environment. Additionally, our method achieved an accuracy of 89.5% even when trained and evaluated on different people. We also report the analyses of accuracies in various conditions and limitations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
