WiGAN: A WiFi Based Gesture Recognition System with GANs
In recent years, a series of research experiments have been conducted on WiFi-based gesture recognition. However, current recognition systems are still facing the challenge of small samples and environmental dependence. To deal with the problem of performance degradation caused by these factors, we...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/17/4757 |
| _version_ | 1797556155603484672 |
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| author | Dehao Jiang Mingqi Li Chunling Xu |
| author_facet | Dehao Jiang Mingqi Li Chunling Xu |
| author_sort | Dehao Jiang |
| collection | DOAJ |
| description | In recent years, a series of research experiments have been conducted on WiFi-based gesture recognition. However, current recognition systems are still facing the challenge of small samples and environmental dependence. To deal with the problem of performance degradation caused by these factors, we propose a WiFi-based gesture recognition system, WiGAN, which uses Generative Adversarial Network (GAN) to extract and generate gesture features. With GAN, WiGAN expands the data capacity to reduce time cost and increase sample diversity. The proposed system extracts and fuses multiple convolutional layer feature maps as gesture features before gesture recognition. After fusing features, Support Vector Machine (SVM) is exploited for human activity classification because of its accuracy and convenience. The key insight of WiGAN is to generate samples and merge multi-grained feature maps in our designed GAN, which not only enhances the data but also allows the neural network to select different grained features for gesture recognition. According to the result of experiments conducted on two existing datasets, the average recognition accuracy of WiGAN reaches 98% and 95.6%, respectively, outperforming the existing system. Moreover, the recognition accuracy under different experimental environments and different users shows the robustness of WiGAN. |
| first_indexed | 2024-03-10T16:57:57Z |
| format | Article |
| id | doaj.art-54da5840aec84d0d8e6e95b625030499 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T16:57:57Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-54da5840aec84d0d8e6e95b6250304992023-11-20T11:03:23ZengMDPI AGSensors1424-82202020-08-012017475710.3390/s20174757WiGAN: A WiFi Based Gesture Recognition System with GANsDehao Jiang0Mingqi Li1Chunling Xu2Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, ChinaIn recent years, a series of research experiments have been conducted on WiFi-based gesture recognition. However, current recognition systems are still facing the challenge of small samples and environmental dependence. To deal with the problem of performance degradation caused by these factors, we propose a WiFi-based gesture recognition system, WiGAN, which uses Generative Adversarial Network (GAN) to extract and generate gesture features. With GAN, WiGAN expands the data capacity to reduce time cost and increase sample diversity. The proposed system extracts and fuses multiple convolutional layer feature maps as gesture features before gesture recognition. After fusing features, Support Vector Machine (SVM) is exploited for human activity classification because of its accuracy and convenience. The key insight of WiGAN is to generate samples and merge multi-grained feature maps in our designed GAN, which not only enhances the data but also allows the neural network to select different grained features for gesture recognition. According to the result of experiments conducted on two existing datasets, the average recognition accuracy of WiGAN reaches 98% and 95.6%, respectively, outperforming the existing system. Moreover, the recognition accuracy under different experimental environments and different users shows the robustness of WiGAN.https://www.mdpi.com/1424-8220/20/17/4757wirelessgesture recognitionchannel status informationgenerate adversarial networksupport vector machine |
| spellingShingle | Dehao Jiang Mingqi Li Chunling Xu WiGAN: A WiFi Based Gesture Recognition System with GANs Sensors wireless gesture recognition channel status information generate adversarial network support vector machine |
| title | WiGAN: A WiFi Based Gesture Recognition System with GANs |
| title_full | WiGAN: A WiFi Based Gesture Recognition System with GANs |
| title_fullStr | WiGAN: A WiFi Based Gesture Recognition System with GANs |
| title_full_unstemmed | WiGAN: A WiFi Based Gesture Recognition System with GANs |
| title_short | WiGAN: A WiFi Based Gesture Recognition System with GANs |
| title_sort | wigan a wifi based gesture recognition system with gans |
| topic | wireless gesture recognition channel status information generate adversarial network support vector machine |
| url | https://www.mdpi.com/1424-8220/20/17/4757 |
| work_keys_str_mv | AT dehaojiang wiganawifibasedgesturerecognitionsystemwithgans AT mingqili wiganawifibasedgesturerecognitionsystemwithgans AT chunlingxu wiganawifibasedgesturerecognitionsystemwithgans |