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Activity Recognition Using the Joint of Wi-Fi 2.4G and 5G Frequency Bands
Human activity recognition based on Wi-Fi Channel State Information (CSI) is playing an increasingly important role in various fields such as security, medical care, etc. Most existing CSI-based activity identification methods rely on a single frequency band of Wi-Fi signals. In addition, the use of...
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| creator | Yan, BeiMing Cheng, Wei Huang, GeTong Zhu, Zhong Shang Gao, Xiang |
| description | Human activity recognition based on Wi-Fi Channel State Information (CSI) is playing an increasingly important role in various fields such as security, medical care, etc. Most existing CSI-based activity identification methods rely on a single frequency band of Wi-Fi signals. In addition, the use of deep learning methods for CSI-based activity recognition is still in its infancy. In this paper, we propose a scheme of activity recognition using the joint CSI of the 2.4G frequency band and the 5G frequency band (ARJF), which takes advantage of a novel convolutional neural network (CNN) to automatically extract deep features from the CSI data, to realize the detection and classification of 7 actions. Compared with the recognition result of a single frequency band, the proposed method has better recognition accuracy. |
| doi_str_mv | 10.1109/ICCT52962.2021.9657966 |
| format | conference_proceeding |
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Compared with the recognition result of a single frequency band, the proposed method has better recognition accuracy.</description><subject>2.4G</subject><subject>5G mobile communication</subject><subject>Activity recognition</subject><subject>Convolutional neural networks</subject><subject>CSI</subject><subject>Deep learning</subject><subject>Feature extraction</subject><subject>Medical services</subject><subject>Performance evaluation</subject><issn>2576-7828</issn><isbn>9781665432061</isbn><isbn>1665432063</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2021</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj91KwzAYQKMgOGafQJC8QGu-_OdyFlsrA0EqXo42-TIj2mpbhb29A3d14FwcOITcACsAmLttyrJV3GlecMahcFoZp_UZyZyxoLWSgjMN52TFldG5sdxekmye3xljAqxzzK1Is_FL-k3LgT6jH_dDWtI40Jc5DXu6vCF9HNOw0DHS15RXifJC1rQbAlU1rSb8_sHBH-jd0cxX5CJ2HzNmJ65JW9235UO-faqbcrPNk7Q214I76Lteq4jR-2BQKdEZjtYjYAjMR24QQo8SuuMlRCklQAhKR8ENiDW5_s8mRNx9Temzmw6707v4A2IgTJc</recordid><startdate>20211013</startdate><enddate>20211013</enddate><creator>Yan, BeiMing</creator><creator>Cheng, Wei</creator><creator>Huang, GeTong</creator><creator>Zhu, Zhong Shang</creator><creator>Gao, Xiang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20211013</creationdate><title>Activity Recognition Using the Joint of Wi-Fi 2.4G and 5G Frequency Bands</title><author>Yan, BeiMing ; Cheng, Wei ; Huang, GeTong ; Zhu, Zhong Shang ; Gao, Xiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i488-63291bab65fefccd7e553a72e8ce1edd0cf27e1dbe41a1101f44411dd56f32713</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2021</creationdate><topic>2.4G</topic><topic>5G mobile communication</topic><topic>Activity recognition</topic><topic>Convolutional neural networks</topic><topic>CSI</topic><topic>Deep learning</topic><topic>Feature extraction</topic><topic>Medical services</topic><topic>Performance evaluation</topic><toplevel>online_resources</toplevel><creatorcontrib>Yan, BeiMing</creatorcontrib><creatorcontrib>Cheng, Wei</creatorcontrib><creatorcontrib>Huang, GeTong</creatorcontrib><creatorcontrib>Zhu, Zhong Shang</creatorcontrib><creatorcontrib>Gao, Xiang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore / Electronic Library Online (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yan, BeiMing</au><au>Cheng, Wei</au><au>Huang, GeTong</au><au>Zhu, Zhong Shang</au><au>Gao, Xiang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Activity Recognition Using the Joint of Wi-Fi 2.4G and 5G Frequency Bands</atitle><btitle>2021 IEEE 21st International Conference on Communication Technology (ICCT)</btitle><stitle>ICCT</stitle><date>2021-10-13</date><risdate>2021</risdate><spage>1266</spage><epage>1270</epage><pages>1266-1270</pages><eissn>2576-7828</eissn><eisbn>9781665432061</eisbn><eisbn>1665432063</eisbn><abstract>Human activity recognition based on Wi-Fi Channel State Information (CSI) is playing an increasingly important role in various fields such as security, medical care, etc. Most existing CSI-based activity identification methods rely on a single frequency band of Wi-Fi signals. In addition, the use of deep learning methods for CSI-based activity recognition is still in its infancy. In this paper, we propose a scheme of activity recognition using the joint CSI of the 2.4G frequency band and the 5G frequency band (ARJF), which takes advantage of a novel convolutional neural network (CNN) to automatically extract deep features from the CSI data, to realize the detection and classification of 7 actions. Compared with the recognition result of a single frequency band, the proposed method has better recognition accuracy.</abstract><pub>IEEE</pub><doi>10.1109/ICCT52962.2021.9657966</doi><tpages>5</tpages></addata></record> |
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| identifier | EISSN: 2576-7828 |
| ispartof | 2021 IEEE 21st International Conference on Communication Technology (ICCT), 2021, p.1266-1270 |
| issn | 2576-7828 |
| language | eng |
| recordid | cdi_ieee_primary_9657966 |
| source | IEEE Xplore All Conference Series |
| subjects | 2.4G 5G mobile communication Activity recognition Convolutional neural networks CSI Deep learning Feature extraction Medical services Performance evaluation |
| title | Activity Recognition Using the Joint of Wi-Fi 2.4G and 5G Frequency Bands |
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