hear-your-action: human action recognition by ultrasound active sensing
Risako Tanigawa Yasunori Ishii
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.