{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:46:50Z","timestamp":1776084410557,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Sciences Foundation of China","award":["62071061"],"award-info":[{"award-number":["62071061"]}]},{"name":"Beijing Institute of Technology Research Fund Program for Young Scholars","award":["62071061"],"award-info":[{"award-number":["62071061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wi-Fi-based human activity recognition has attracted broad attention for its advantages, which include being device-free, privacy-protected, unaffected by light, etc. Owing to the development of artificial intelligence techniques, existing methods have made great improvements in sensing accuracy. However, the performance of multi-location recognition is still a challenging issue. According to the principle of wireless sensing, wireless signals that characterize activity are also seriously affected by location variations. Existing solutions depend on adequate data samples at different locations, which are labor-intensive. To solve the above concerns, we present an amplitude- and phase-enhanced deep complex network (AP-DCN)-based multi-location human activity recognition method, which can fully utilize the amplitude and phase information simultaneously so as to mine more abundant information from limited data samples. Furthermore, considering the unbalanced sample number at different locations, we propose a perception method based on the deep complex network-transfer learning (DCN-TL) structure, which effectively realizes knowledge sharing among various locations. To fully evaluate the performance of the proposed method, comprehensive experiments have been carried out with a dataset collected in an office environment with 24 locations and five activities. The experimental results illustrate that the approaches can achieve 96.85% and 94.02% recognition accuracy, respectively.<\/jats:p>","DOI":"10.3390\/s22166178","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T23:28:41Z","timestamp":1660865321000},"page":"6178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Device-Free Multi-Location Human Activity Recognition Using Deep Complex Network"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3453-8437","authenticated-orcid":false,"given":"Xue","family":"Ding","sequence":"first","affiliation":[{"name":"Mobile and Terminal Technology Research Department, China Telecom Research Institute, Beijing 102209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlei","family":"Hu","sequence":"additional","affiliation":[{"name":"Mobile and Terminal Technology Research Department, China Telecom Research Institute, Beijing 102209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiliang","family":"Xie","sequence":"additional","affiliation":[{"name":"Mobile and Terminal Technology Research Department, China Telecom Research Institute, Beijing 102209, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9309-3407","authenticated-orcid":false,"given":"Yi","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8075-0439","authenticated-orcid":false,"given":"Jianfei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3598-3804","authenticated-orcid":false,"given":"Ting","family":"Jiang","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":[[2022,8,18]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Human Activity Recognition with Deep Learning: Overview, Challenges & Possibilities","volume":"339","author":"Kumar","year":"2021","journal-title":"Ccf Trans. 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