VBCNet: A Hybird Network for Human Activity Recognition
In recent years, the research on human activity recognition based on channel state information (CSI) of Wi-Fi has gradually attracted much attention in order to avoid the deployment of additional devices and reduce the risk of personal privacy leakage. In this paper, we propose a hybrid network arch...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7793 |
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| author | Fei Ge Zhenyang Dai Zhimin Yang Fei Wu Liansheng Tan |
| author_facet | Fei Ge Zhenyang Dai Zhimin Yang Fei Wu Liansheng Tan |
| author_sort | Fei Ge |
| collection | DOAJ |
| description | In recent years, the research on human activity recognition based on channel state information (CSI) of Wi-Fi has gradually attracted much attention in order to avoid the deployment of additional devices and reduce the risk of personal privacy leakage. In this paper, we propose a hybrid network architecture, named VBCNet, that can effectively identify human activity postures. Firstly, we extract CSI sequences from each antenna of Wi-Fi signals, and the data are preprocessed and tokenised. Then, in the encoder part of the model, we introduce a layer of long short-term memory network to further extract the temporal features in the sequences and enhance the ability of the model to capture the temporal information. Meanwhile, VBCNet employs a convolutional feed-forward network instead of the traditional feed-forward network to enhance the model’s ability to process local and multi-scale features. Finally, the model classifies the extracted features into human behaviours through a classification layer. To validate the effectiveness of VBCNet, we conducted experimental evaluations on the classical human activity recognition datasets UT-HAR and Widar3.0 and achieved an accuracy of 98.65% and 77.92%. These results show that VBCNet exhibits extremely high effectiveness and robustness in human activity recognition tasks in complex scenarios. |
| format | Article |
| id | doaj-art-708c82db24dc4df2a6ada1356b30cf84 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-708c82db24dc4df2a6ada1356b30cf842025-08-20T02:50:37ZengMDPI AGSensors1424-82202024-12-012423779310.3390/s24237793VBCNet: A Hybird Network for Human Activity RecognitionFei Ge0Zhenyang Dai1Zhimin Yang2Fei Wu3Liansheng Tan4School of Computer Science, Central China Normal University, Wuhan 430070, ChinaSchool of Computer Science, Central China Normal University, Wuhan 430070, ChinaSchool of Computer Science, Central China Normal University, Wuhan 430070, ChinaSchool of Computer Science, Central China Normal University, Wuhan 430070, ChinaSchool of Computer Science, Central China Normal University, Wuhan 430070, ChinaIn recent years, the research on human activity recognition based on channel state information (CSI) of Wi-Fi has gradually attracted much attention in order to avoid the deployment of additional devices and reduce the risk of personal privacy leakage. In this paper, we propose a hybrid network architecture, named VBCNet, that can effectively identify human activity postures. Firstly, we extract CSI sequences from each antenna of Wi-Fi signals, and the data are preprocessed and tokenised. Then, in the encoder part of the model, we introduce a layer of long short-term memory network to further extract the temporal features in the sequences and enhance the ability of the model to capture the temporal information. Meanwhile, VBCNet employs a convolutional feed-forward network instead of the traditional feed-forward network to enhance the model’s ability to process local and multi-scale features. Finally, the model classifies the extracted features into human behaviours through a classification layer. To validate the effectiveness of VBCNet, we conducted experimental evaluations on the classical human activity recognition datasets UT-HAR and Widar3.0 and achieved an accuracy of 98.65% and 77.92%. These results show that VBCNet exhibits extremely high effectiveness and robustness in human activity recognition tasks in complex scenarios.https://www.mdpi.com/1424-8220/24/23/7793Wi-Fi channel state informationbody-coordinate velocity profileViTBiLSTMconvolutional feed-forward |
| spellingShingle | Fei Ge Zhenyang Dai Zhimin Yang Fei Wu Liansheng Tan VBCNet: A Hybird Network for Human Activity Recognition Sensors Wi-Fi channel state information body-coordinate velocity profile ViT BiLSTM convolutional feed-forward |
| title | VBCNet: A Hybird Network for Human Activity Recognition |
| title_full | VBCNet: A Hybird Network for Human Activity Recognition |
| title_fullStr | VBCNet: A Hybird Network for Human Activity Recognition |
| title_full_unstemmed | VBCNet: A Hybird Network for Human Activity Recognition |
| title_short | VBCNet: A Hybird Network for Human Activity Recognition |
| title_sort | vbcnet a hybird network for human activity recognition |
| topic | Wi-Fi channel state information body-coordinate velocity profile ViT BiLSTM convolutional feed-forward |
| url | https://www.mdpi.com/1424-8220/24/23/7793 |
| work_keys_str_mv | AT feige vbcnetahybirdnetworkforhumanactivityrecognition AT zhenyangdai vbcnetahybirdnetworkforhumanactivityrecognition AT zhiminyang vbcnetahybirdnetworkforhumanactivityrecognition AT feiwu vbcnetahybirdnetworkforhumanactivityrecognition AT lianshengtan vbcnetahybirdnetworkforhumanactivityrecognition |