{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:28:29Z","timestamp":1771518509225,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless sensing can be used for human identification by mining and quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric (DFB) system, WirelessID, that explores the joint human fine-grained behavior and body physical signatures embedded in channel state information (CSI) by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to the identification performance. Inspired by the success of the attention mechanism in computer vision (CV), thus, to extract more robust features, we introduce the spatiotemporal attention function into our system. To evaluate the performance, commercial WiFi devices are used for prototyping WirelessID in a real laboratory environment with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.<\/jats:p>","DOI":"10.3390\/s21175921","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T13:18:26Z","timestamp":1630934306000},"page":"5921","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Device-Free Human Identification Using Behavior Signatures in WiFi Sensing"],"prefix":"10.3390","volume":"21","author":[{"given":"Ronghui","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2517","DOI":"10.1109\/JIOT.2020.3024234","article-title":"Device-Free Wireless Sensing for Human Detection: The Deep Learning Perspective","volume":"8","author":"Zhang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.patrec.2015.12.013","article-title":"50 years of biometric research: Accomplishments, challenges, and opportunities","volume":"79","author":"Jain","year":"2016","journal-title":"Pattern Recognit. 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