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Existing RFID-based gesture recognition systems typically require users to collect a large amount of training data for each gesture class, and once a new class is introduced, the entire recognition model needs to be retrained. This greatly limits their scalability for new gestures. In this article, we propose FewSense, a practical RFID sensing system that achieves accurate gesture recognition with a small number of training samples. To provide sufficient training samples for FewSense, we introduce a virtual sample generation method to achieve data augmentation. Based on the augmented training data, FewSense enables few-shot gesture recognition. With the introduction of a fine-tuning mechanism, FewSense can easily adapt to changing gesture classes. Real-world experiments demonstrate that even with only seven training samples, FewSense achieves 90% recognition accuracy.<\/jats:p>","DOI":"10.1145\/3748725","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T11:21:13Z","timestamp":1752751273000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["FewSense: Enabling Few-Shot Gesture Recognition via COTS RFID"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3276-9939","authenticated-orcid":false,"given":"Xiaobin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7924-2840","authenticated-orcid":false,"given":"Hongzhe","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3041-130X","authenticated-orcid":false,"given":"Wei","family":"Gao","sequence":"additional","affiliation":[{"name":"Shandong Institute of Scientific and Technical Information","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1389-0068","authenticated-orcid":false,"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,24]]},"reference":[{"issue":"3","key":"e_1_3_1_2_2","first-page":"33:1\u201333:33","article-title":"A tutorial on human activity recognition using body-worn inertial sensors","volume":"46","author":"Bulling Andreas","year":"2014","unstructured":"Andreas Bulling, Ulf Blanke, and Bernt Schiele. 2014. 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