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Netw."],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>\n            Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present\n            <jats:italic>GaitSense<\/jats:italic>\n            , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds,\n            <jats:italic>GaitSense<\/jats:italic>\n            first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis,\n            <jats:italic>GaitSense<\/jats:italic>\n            reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques.\n            <jats:italic>GaitSense<\/jats:italic>\n            also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.\n          <\/jats:p>","DOI":"10.1145\/3466638","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T20:05:13Z","timestamp":1633464313000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["GaitSense: Towards Ubiquitous Gait-Based Human Identification with Wi-Fi"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5179-3202","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Qian","sequence":"additional","affiliation":[{"name":"University of California San Diego, San Diego, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Qian","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818072"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of ICBT.","author":"Banka Asif Ali","year":"2010","unstructured":"Asif Ali Banka , Dr Ajaz , and Hussain Mir . 2010 . 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