CGAC:a CSI-based human activity recognition method
Channel state information(CSI)of WiFi has a wide range of applications in the field of human action recognition(HAR).Most methods of CSI-based HAR are deficient in accuracy and lack robustness in different environments.To address these issues,this paper proposes a composite human action recognition model(CGAC)that combines convolutional neural networks(CNNs),gated recurrent units,and attention mechanisms.First,temporal features are extracted from the input data using CNNs.Second,the feature size is reduced by the pooling operation.Third,the temporal features are modeled by using BiGRU.Thus,the attention to the key features is enhanced by the attention mechanism.Experiments are conducted on three public datasets,and the results show that CGAC obtains a higher accuracy than that of any other existing methods:99.70％accuracy on the UT-HAR dataset,97.50％on the HAR dataset of NTU-Fi,and 97.81％on the Human-ID dataset,validating its effectiveness.
human activity recognition(HAR)channel state information(CSI)deep learningconvolutional neural network(CNN)gate recurrent unit(GRU)attention mechanism
国家科技期刊平台
NSTL
万方数据
