{"title":"Cross-domain human activity recognition using reconstructed Wi-Fi signal","authors":"Xingcan Chen","doi":"10.1016/j.phycom.2025.102651","DOIUrl":null,"url":null,"abstract":"<div><div>Recent studies have shown that Wi-Fi channel state information (CSI) based approaches for human activity recognition (HAR) is successful. However, the performance of these approaches often deteriorate when deployed to a new industrial environment. To solve this problem without retraining, we present a novel Wi-Fi CSI tensor based cross-domain HAR approach (TensFi). Specifically, activity-related CSI is first separated from the original CSI through an ensemble empirical mode decomposition (EEMD) algorithm. Then, the sparse signal representation (SSP) algorithm is used to extract partial CSI sub-carriers that are more relevant to human activities. Furthermore, the sparse CSI is modeled as phase-integrated CSI (PI-CSI) and further reconstructed as a CSI tensor with unique decomposition. After that, CANDECOMP/PARAFAC (CP) is used to decompose the reconstructed CSI tensor. Finally, a multi-head self-attention based gated temporal convolutional network (MAGTCN) is designed to capture the features of decomposed CSI tensor and then make the final activity decision. Experimental results show that TensFi can achieve good cross-domain generalization performance.</div></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"71 ","pages":"Article 102651"},"PeriodicalIF":2.0000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1874490725000540","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Recent studies have shown that Wi-Fi channel state information (CSI) based approaches for human activity recognition (HAR) is successful. However, the performance of these approaches often deteriorate when deployed to a new industrial environment. To solve this problem without retraining, we present a novel Wi-Fi CSI tensor based cross-domain HAR approach (TensFi). Specifically, activity-related CSI is first separated from the original CSI through an ensemble empirical mode decomposition (EEMD) algorithm. Then, the sparse signal representation (SSP) algorithm is used to extract partial CSI sub-carriers that are more relevant to human activities. Furthermore, the sparse CSI is modeled as phase-integrated CSI (PI-CSI) and further reconstructed as a CSI tensor with unique decomposition. After that, CANDECOMP/PARAFAC (CP) is used to decompose the reconstructed CSI tensor. Finally, a multi-head self-attention based gated temporal convolutional network (MAGTCN) is designed to capture the features of decomposed CSI tensor and then make the final activity decision. Experimental results show that TensFi can achieve good cross-domain generalization performance.
期刊介绍:
PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published.
Topics of interest include but are not limited to:
Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.