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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2024,8,22]]},"abstract":"<jats:p>In this paper, we aim to study millimeter-wave-based gait recognition in complex indoor environments, focusing on dealing with multipath ghosts and supporting rapid deployment to new environments. We design a ghost detection algorithm based on velocity change patterns. This algorithm relies solely on velocity estimation, requiring no environmental priors or multipath modeling. Hence, it is suitable for single-chip millimeter-wave radar with low angular resolution and can be conveniently deployed in new indoor settings. In addition, we build a gait recognition network based on an attention-based Recurrent Neural Network (RNN) to extract spatiotemporal-velocity features from RD heatmaps.<\/jats:p>\n          <jats:p>We have evaluated RDGait in two scenarios: a corridor scenario and a crowded office scenario, with 125 volunteers of different genders and ages ranging from 6 to 63. RDGait achieves a user recognition accuracy exceeding 95% among 125 candidates in both scenarios. We have further deployed RDGait in two additional scenarios using the pretrain-finetune approach. With minimal user registration data, RDGait could achieve satisfactory (&gt; 90%) recognition accuracy in these new environments considering different radar placements, heights, and number of co-existing users.<\/jats:p>","DOI":"10.1145\/3678552","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T14:36:21Z","timestamp":1725892581000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["RDGait: A mmWave Based Gait User Recognition System for Complex Indoor Environments Using Single-chip Radar"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0877-4636","authenticated-orcid":false,"given":"Dequan","family":"Wang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1589-4222","authenticated-orcid":false,"given":"Xinran","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8860-9042","authenticated-orcid":false,"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5524-1025","authenticated-orcid":false,"given":"Lingyu","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6368-9250","authenticated-orcid":false,"given":"Xiaoran","family":"Fan","sequence":"additional","affiliation":[{"name":"Google, Irvine, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9046-798X","authenticated-orcid":false,"given":"Yanyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, Anhui, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"The Kendall rank correlation coefficient. 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