Abstract
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.
基金项目
Strategic Priority Research Program of Chinese Academy of Sciences(XDC02040300)
维普
万方数据