App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 6

Csi-based few-shot learning and domain adaptation gesture recognition model

Ouyang Shaoxiong1
Wang Yang1
Hou Hailun1
Xu Jiawei1,2
Zhang Shikun1
Zhao Congyu1
Cao Lixiang1
1. School of Computer and Information, Anhui Normal University, Wuhu Anhui 241000, China
2. School of Computer and Information, Hefei University of Technology, Hefei 230000, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0404

Abstract

Wi-Fi sensing plays a vital role in contactless perception tasks such as gesture recognition, offering advantages in privacy preservation, low deployment cost, and strong signal penetration. To overcome the domain shift problem inherent in Wi-Fi sensing, this study proposes a few-shot learning–based domain-adaptive gesture recognition model, named FCDG-FI. First, we design a domain-adaptive feature extraction module that consists of a feature extractor and a domain discriminator. Second, we integrate an efficient attention mechanism into the conventional convolutional backbone to enhance the extraction of subtle yet critical features. Third, we employ a domain-adversarial training strategy to learn domain-invariant gesture representations from Channel State Information (CSI) data, thereby achieving robust cross-domain recognition. Finally, we conduct extensive experiments on the SignFi dataset, a subset of Widar3.0, and a self-collected dataset under both intra-domain and cross-domain settings to evaluate the effectiveness of FCDG-FI. Experimental results show that FCDG-FI consistently outperforms existing methods across multiple tasks and datasets, and recognizes previously unseen gesture classes, effectively mitigating the challenges caused by domain shifts. The source code is available at: https://github.com/Truthmm/FCDG-FI.

Foundation Support

国家自然科学基金资助项目(61871412)
中国农业农村部区块链农业应用重点实验室开放基金资助项目(2023KLABA04)
安徽省高等学校科学研究重点项目(2023AH052757)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0404
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 6

Publish History

[2026-02-04] Accepted Paper

Cite This Article

欧阳少雄, 王杨, 后海伦, 等. 基于CSI小样本学习的域自适应手势识别模型 [J]. 计算机应用研究, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0404. (Ouyang Shaoxiong, Wang Yang, Hou Hailun, et al. Csi-based few-shot learning and domain adaptation gesture recognition model [J]. Application Research of Computers, 2026, 43 (6). (2026-02-25). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0404. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)