{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T07:21:12Z","timestamp":1757575272610,"version":"3.41.0"},"reference-count":28,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,5,11]],"date-time":"2024-05-11T00:00:00Z","timestamp":1715385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>Gait is regarded as a unique feature for identifying people, and gait recognition is the basis of various customized services of the IoT. Unlike traditional techniques for identifying people, the Wi-Fi-based technique is unconstrained by illumination conditions and such that it eliminates the need for dense, specialized sensors and wearable devices. Although deep learning-based sensing models are conducive to the development of Wi-Fi-based identification, the latter technique relies on a large amount of data and requires a long training time, where this limits the scope of its use for identifying people. In this study, we propose a Wi-Fi sensing model called Wave-CapNet for human identification. We use data processing to eliminate errors in the raw data so that the model can extract the characteristics in channel state information (CSI). We also design a dedicated adaptive wavelet neural network to extract representative features from Wi-Fi signals with only a few epochs of training and a small number of parameters. Experiments show that it can identify human gait with an average accuracy of 99%. Moreover, it can achieve an average accuracy of 95% by using only 10% of the data and fewer than five epochs and outperforms state-of-the-art (SOTA) methods.<\/jats:p>","DOI":"10.1145\/3624746","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T11:37:05Z","timestamp":1695123425000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Wave-CapNet: A Wavelet Neuron-based Wi-Fi Sensing Model for Human Identification"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4985-5213","authenticated-orcid":false,"given":"Zhiyi","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Software Technology, Dalian University of Technology, Dalian City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1810-3019","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software Technology, Dalian University of Technology and Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7896-7399","authenticated-orcid":false,"given":"Xinxin","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Software Technology, Dalian University of Technology, Dalian City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4141-1699","authenticated-orcid":false,"given":"Yu","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Software Technology, Dalian University of Technology, Dalian City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0889-2907","authenticated-orcid":false,"given":"Jian","family":"Fang","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4378-6539","authenticated-orcid":false,"given":"Bingxian","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Software Technology, Dalian University of Technology and Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian City, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,11]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2018.01.007"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/HNICEM.2017.8269432"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3267305.3277832"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2545669"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2361287"},{"issue":"1","key":"e_1_3_1_7_2","first-page":"1","article-title":"Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors","volume":"14","author":"Al Naimi Ibrahim","year":"2017","unstructured":"Naimi Ibrahim Al, Wong Chi Biu, Moore Philip, and Chen Xi. 2017. Multimodal approach for non-tagged indoor identification and tracking using smart floor and pyroelectric infrared sensors. Int. J. Comput. Sci. Eng. 14, 1 (2017), 1\u201315.","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"e_1_3_1_8_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Sabour Sara","year":"2017","unstructured":"Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton. 2017. Dynamic routing between capsules. In Proceedings of the Advances in Neural Information Processing Systems. Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/2cad8fa47bbef282badbb8de5374b894-Paper.pdf"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","unstructured":"Yunze Zeng Parth Pathak and Prasant Mohapatra. 2016. WiWho: WiFi-based person identification in smart spaces. In Proceedings of the 2016 15th ACM\/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE 1\u201312. DOI:10.1109\/IPSN.2016.7460727","DOI":"10.1109\/IPSN.2016.7460727"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/DCOSS.2016.30"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCECE54139.2022.9712812"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971670"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2953488"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2019.05.005"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9014226"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/IECON.2018.8591820"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3466638"},{"issue":"004","key":"e_1_3_1_18_2","first-page":"122","article-title":"A deep learning algorithm for contactless human identification","volume":"053","author":"Yu XingDa","year":"2019","unstructured":"XingDa Yu, WenJie Chen, Ding Wang, Yangjie Cao, and HuiHui Chen. 2019. A deep learning algorithm for contactless human identification. J. Xian Jiaotong Univ. 053, 004 (2019), 122\u2013127.","journal-title":"J. Xian Jiaotong Univ."},{"key":"e_1_3_1_19_2","first-page":"1","article-title":"WiNet: A gait recognition model suitable for wireless sensing scene","volume":"07","author":"Duan PengSong","year":"2020","unstructured":"PengSong Duan, ZhiYi Zhou, Chao Wang, YangJie Cao, and EnDong Wang. 2020. WiNet: A gait recognition model suitable for wireless sensing scene. J. Xian Jiaotong Univ. 07 (2020), 1\u201310.","journal-title":"J. Xian Jiaotong Univ."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/SAHCN.2018.8397108"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3084041.3084061"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/2590296.2590321"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3078782"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3040782"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3156099"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1137\/0515056"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2934106"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.182697"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/1925861.1925870"}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3624746","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3624746","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:35:44Z","timestamp":1750178144000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3624746"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,11]]},"references-count":28,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7,31]]}},"alternative-id":["10.1145\/3624746"],"URL":"https:\/\/doi.org\/10.1145\/3624746","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"type":"print","value":"1550-4859"},{"type":"electronic","value":"1550-4867"}],"subject":[],"published":{"date-parts":[[2024,5,11]]},"assertion":[{"value":"2022-12-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-11","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-05-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}