南京邮电大学学报（自然科学版）2024，Vol.44Issue(6)：12-24,13.DOI:10.14132/j.cnki.1673-5439.2024.06.002
CGAC:一种基于CSI的人体动作识别方法
CGAC:a CSI-based human activity recognition method
摘要
Abstract
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.关键词
人体动作识别/信道状态信息/深度学习/卷积神经网络/门控循环单元/注意力机制Key words
human activity recognition(HAR)/channel state information(CSI)/deep learning/convolutional neural network(CNN)/gate recurrent unit(GRU)/attention mechanism分类
信息技术与安全科学引用本文复制引用
苏健,郑毓煌,陈思光..CGAC:一种基于CSI的人体动作识别方法[J].南京邮电大学学报（自然科学版）,2024,44(6):12-24,13.基金项目
国家自然科学基金(61802196)资助项目 （61802196）
