Self-Attention Mechanism-Based Activity and Motion Recognition Using Wi-Fi Signals  

作　　者：Kabo Poloko Nkabiti Chen Yueyun Tang Chao 

机构地区：[1]School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China [2]Shunde Innovation School,University of Science and Technology Beijing,Guangdong 528399,China

出　　处：《China Communications》2024年第12期92-107,共16页中国通信（英文版）

基　　金：This work was supported by Foshan Science and Technology Innovation Special Fund Project(No.BK22BF004 and No.BK20AF004),Guangdong Province,China.

摘　　要：Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent years.Many research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of things.Nevertheless,most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth sequential computation.Therefore,in this paper,we propose a deep-learning approach based solely on attention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi signals.The Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI data.Experiments were carried out to evaluate the performance of the proposed Sole-SAM architecture.The experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory(LSTM)and Recurrent Neural Network(RNN).Sole-SAM archived a 0.94%accuracy level,which is 0.04%better than RNN and 0.02%better than LSTM.

关 键 词：CSI human activity and motion recognition Sole-SAM WI-FI 

分 类 号：TN92[电子电信—通信与信息系统]