2023/09/15 by Risako Tanigawa, Tanigawa, Risako, Yasunori Ishii +1 · 1 citation
Computer Science · Medicine · #Anomaly Detection Techniques and Applications #Artificial Intelligence in Healthcare and Education #Audio and Speech Processing (eess.AS) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #FOS: Electrical engineering #Sound (cs.SD) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2309.08087
openalex publication_date 2023/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.