首页|TransTM:A device-free method based on time-streaming multiscale transformer for human activity recognition
TransTM:A device-free method based on time-streaming multiscale transformer for human activity recognition
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
RFID-based human activity recognition(HAR)attracts attention due to its convenience,non-invasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes single-human activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behavior-based classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1％).The dataset we collected for detecting RFID-based indoor human activities will be published.
Human activity recognitionRFIDTransformer
Yi Liu、Weiqing Huang、Shang Jiang、Bobai Zhao、Shuai Wang、Siye Wang、Yanfang Zhang
展开 >
Institute of Information Engineering Chinese Academy of Sciences,School of Cyber Security,University of Chinese Academy of Sciences,Beijing,China
School of Information Management,Beijing Information Science and Technology University,Beijing,China
Strategic Priority Research Program of Chinese Academy of Sciences
万方数据
维普
