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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 privateinformation, such as visible faces and scene backgrounds, which are notnecessary for recognizing user action. In this paper, we propose aprivacy-preserving action recognition by ultrasound active sensing. As actionrecognition from ultrasound active sensing in a non-invasive manner is not wellinvestigated, we create a new dataset for action recognition and conduct acomparison of features for classification. We calculated feature values byfocusing on the temporal variation of the amplitude of ultrasound reflectedwaves and performed classification using a support vector machine and VGG foreight fundamental action classes. We confirmed that our method achieved anaccuracy of 97.9% when trained and evaluated on the same person and in the sameenvironment. Additionally, our method achieved an accuracy of 89.5% even whentrained and evaluated on different people. We also report the analyses ofaccuracies in various conditions and limitations.

hear-your-action: human action recognition by ultrasound active sensing