基于CSI实例标准化的域泛化人体动作识别模型

扫码查看

原文链接

国家科技期刊平台
NETL
NSTL
万方数据

中文摘要：为了实现完全不依赖目标域数据的Wi-Fi跨域人体动作感知,提出了一种基于CSI实例标准化的域泛化人体动作识别模型INDG-Fi.INDG-Fi使用实例标准化去除CSI特征表示的领域信息,接着构建共享特征提取的动作分类器和域分类器,并通过动作偏向学习和对抗性的域学习,将编码层提取的特征偏向人体动作引起的信号特征,同时远离领域信号影响.为了让模型关注受人体动作影响更显著的子载波信号,在编码层中加入子载波注意力模块.实现结果表明,所提INDG-Fi在不可见的用户和位置的感知性能分别为97.99%和92.73%,能够实现鲁棒的跨域感知.

外文标题：Domain-generalization human activity recognition model based on CSI instance normalization

外文摘要：To achieve Wi-Fi cross-domain human activity perception that was not dependent on target domain data,a domain-generalization human activity recognition model based on CSI instance normalization called INDG-Fi was pro-posed.The instance normalization standardization was utilized to remove domain information from the representation of CSI features by INDG-Fi.Then action classifiers and domain classifiers were constructed for shared feature extraction.By employing activity bias learning and adversarial domain learning,the model biased the features extracted from the en-coding layer towards signal variations caused by human actions while moving away from domain signals.To enhance the model's focus on subcarrier signals that were more significantly influenced by human actions,a subcarrier attention mod-ule was incorporated into the encoding layer.The implemented results demonstrate that the proposed INDG-Fi achieves perceptual accuracies of 97.99%and 92.73%for unseen users and locations,respectively,thus enabling robust cross-domain perception.

外文关键词：

channel state informationwireless sensinghuman activity recognitiondomain-generalization

作者：

王杨、许佳炜、王傲、夏慧娟、赵传信、季一木

展开 >

作者单位：

安徽师范大学计算机与信息学院,安徽芜湖 241002

南京邮电大学计算机学院,江苏南京 210003

关键词：

信道状态信息无线感知人体动作识别域泛化

基金：

国家自然科学基金资助项目江苏省重点研发计划基金资助项目安徽省自然科学基金重点项目安徽省自然科学基金重点项目安徽省自然科学基金重点项目

项目编号：

61871412BE2023004-2KJ2019A0938KJ2021A1314KJ2019A0979

出版年：

2024

DOI：

10.11959/j.issn.1000-436x.2024110

通信学报

中国通信学会

通信学报

CSTPCD北大核心

影响因子：1.265

ISSN：1000-436X

年,卷(期)：2024.45(6)