太赫兹科学与电子信息学报2026，Vol.24Issue(1)：73-79,7.DOI:10.11805/TKYDA2024543
基于WiFi的跨环境适应性的人体行为识别算法
Human behavior recognition algorithm based on WiFi cross-environment adaptability
摘要
Abstract
To address the challenges of privacy protection in recognizing unsafe behaviors such as falls and low cross-environment recognition rates,this paper proposes a behavior recognition framework,Single-antenna Cross-environment Stable Human Activity Feature Extraction and Recognition Framework(SSRF),based on Channel State Information(CSI),optimized from the existing ReWiS model.By collecting data on five types of elderly behaviors(such as falls,no action,etc.)from different environments,the CSI signals are normalized,followed by Singular Value Decomposition(SVD)and Pearson correlation coefficient calculation to generate labeled CSI data samples,which are then fed into the ProtoNet model for classification.Compared to ReWiS,SSRF significantly reduces the number of parameters(from 111 936 to 37 392)and accelerates both training and testing speed,with total training time reduced from 33.12 s to 26.8 s,and per-sample testing time reduced from 0.000 149 s to 0.000 104 s.In the four-category task of a public dataset and the five-category task of a custom dataset,SSRF achieves average cross-environment recognition accuracies of 89%and 85%,respectively,with 95%accuracy for fall detection.Experimental results show that SSRF maintains high generalization performance while significantly improving the efficiency.关键词
信道状态信息/人体行为识别/元学习Key words
Channel State Information(CSI)/human behavior recognition/meta-learning分类
信息技术与安全科学引用本文复制引用
李星星,黄景涛,陆许明,陈翔..基于WiFi的跨环境适应性的人体行为识别算法[J].太赫兹科学与电子信息学报,2026,24(1):73-79,7.基金项目
广东普通高校重点领域专项资助项目(2022ZDZX1033) （2022ZDZX1033）
江门市基础与理论科学研究类科技计划资助项目(2022JC01026) （2022JC01026）
