A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU
Human activity sensing based on Wi-Fi channel state information (CSI) has an important application prospect in future intelligent interaction scenarios such as virtual reality, intelligent games, and the metaverse.Accurate sensing of complex and continuous human activities is an important challenge...
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China InfoCom Media Group
2023-12-01
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| Series: | 物联网学报 |
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| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00360/ |
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| author | Yang LIU Anming DONG Jiguo YU Kai ZHAO You ZHOU |
| author_facet | Yang LIU Anming DONG Jiguo YU Kai ZHAO You ZHOU |
| author_sort | Yang LIU |
| collection | DOAJ |
| description | Human activity sensing based on Wi-Fi channel state information (CSI) has an important application prospect in future intelligent interaction scenarios such as virtual reality, intelligent games, and the metaverse.Accurate sensing of complex and continuous human activities is an important challenge for Wi-Fi sensing.Convolutional neural network (CNN) has the ability of spatial feature extraction but is poor at modeling the temporal features of the data.While long short-term memory (LSTM) network or gated recurrent unit (GRU) network, which are suitable for modeling time-series data, neglect learning spatial features of data.In order to solve this problem, an improved CNN that integrates bidirectional gated recurrent unit (BiGRU) network was proposed.The bi-directional feature extraction ability of BiGRU was used to capture the correlation and dependence of the front and back information in the time series data.The extraction of the spatiotemporal features of the time series CSI data was realized, and then the mapping relationship between the action and the CSI data was present.Thus the recognition accuracy of the complex continuous action was improved.The proposed network structure was tested with basketball actions.The results show that the recognition accuracy of this method is above 95% under various conditions.Compared with the traditional multi-layer perceptron (MLP), CNN, LSTM, GRU, and attention based bidirectional long short-term memory (ABLSTM) baseline methods, the recognition accuracy has been improved by 1%～20%. |
| format | Article |
| id | doaj-art-e9671058f6534cf7a2b6f3f3e6b505c2 |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-e9671058f6534cf7a2b6f3f3e6b505c22025-01-15T02:54:23ZzhoChina InfoCom Media Group物联网学报2096-37502023-12-01715316759566429A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRUYang LIUAnming DONGJiguo YUKai ZHAOYou ZHOUHuman activity sensing based on Wi-Fi channel state information (CSI) has an important application prospect in future intelligent interaction scenarios such as virtual reality, intelligent games, and the metaverse.Accurate sensing of complex and continuous human activities is an important challenge for Wi-Fi sensing.Convolutional neural network (CNN) has the ability of spatial feature extraction but is poor at modeling the temporal features of the data.While long short-term memory (LSTM) network or gated recurrent unit (GRU) network, which are suitable for modeling time-series data, neglect learning spatial features of data.In order to solve this problem, an improved CNN that integrates bidirectional gated recurrent unit (BiGRU) network was proposed.The bi-directional feature extraction ability of BiGRU was used to capture the correlation and dependence of the front and back information in the time series data.The extraction of the spatiotemporal features of the time series CSI data was realized, and then the mapping relationship between the action and the CSI data was present.Thus the recognition accuracy of the complex continuous action was improved.The proposed network structure was tested with basketball actions.The results show that the recognition accuracy of this method is above 95% under various conditions.Compared with the traditional multi-layer perceptron (MLP), CNN, LSTM, GRU, and attention based bidirectional long short-term memory (ABLSTM) baseline methods, the recognition accuracy has been improved by 1%～20%.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00360/channel state informationhuman activity sensingcomplex continuous actionconvolutional neural networkbidirectional gated recurrent unit |
| spellingShingle | Yang LIU Anming DONG Jiguo YU Kai ZHAO You ZHOU A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU 物联网学报 channel state information human activity sensing complex continuous action convolutional neural network bidirectional gated recurrent unit |
| title | A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU |
| title_full | A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU |
| title_fullStr | A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU |
| title_full_unstemmed | A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU |
| title_short | A Wi-Fi sensing method for complex continuous human activities based on CNN-BiGRU |
| title_sort | wi fi sensing method for complex continuous human activities based on cnn bigru |
| topic | channel state information human activity sensing complex continuous action convolutional neural network bidirectional gated recurrent unit |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2023.00360/ |
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