WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals
Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a larg...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/1/402 |
| _version_ | 1797497512475492352 |
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| author | Zhanjun Hao Juan Niu Xiaochao Dang Zhiqiang Qiao |
| author_facet | Zhanjun Hao Juan Niu Xiaochao Dang Zhiqiang Qiao |
| author_sort | Zhanjun Hao |
| collection | DOAJ |
| description | Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person’s motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes “cross-personnel” movement recognition with excellent recognition performance. |
| first_indexed | 2024-03-10T03:20:17Z |
| format | Article |
| id | doaj.art-8ad9e9bb6aa341b7a47831488db31a87 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:20:17Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8ad9e9bb6aa341b7a47831488db31a872023-11-23T12:21:42ZengMDPI AGSensors1424-82202022-01-0122140210.3390/s22010402WiPg: Contactless Action Recognition Using Ambient Wi-Fi SignalsZhanjun Hao0Juan Niu1Xiaochao Dang2Zhiqiang Qiao3College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, ChinaMotion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person’s motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes “cross-personnel” movement recognition with excellent recognition performance.https://www.mdpi.com/1424-8220/22/1/402device-free sensingchannel state informationhuman action standard recognitionpersonnel independencegenerative adversarial networkprincipal component analysis |
| spellingShingle | Zhanjun Hao Juan Niu Xiaochao Dang Zhiqiang Qiao WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals Sensors device-free sensing channel state information human action standard recognition personnel independence generative adversarial network principal component analysis |
| title | WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals |
| title_full | WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals |
| title_fullStr | WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals |
| title_full_unstemmed | WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals |
| title_short | WiPg: Contactless Action Recognition Using Ambient Wi-Fi Signals |
| title_sort | wipg contactless action recognition using ambient wi fi signals |
| topic | device-free sensing channel state information human action standard recognition personnel independence generative adversarial network principal component analysis |
| url | https://www.mdpi.com/1424-8220/22/1/402 |
| work_keys_str_mv | AT zhanjunhao wipgcontactlessactionrecognitionusingambientwifisignals AT juanniu wipgcontactlessactionrecognitionusingambientwifisignals AT xiaochaodang wipgcontactlessactionrecognitionusingambientwifisignals AT zhiqiangqiao wipgcontactlessactionrecognitionusingambientwifisignals |