Recurrent ConFormer for WiFi activity recognition
Dear Editor, Human activity recognition (HAR) using WiFi signals has been a significant task due to its potential applications in for example, healthcare services and smart homes. This letter deals with the WiFi channel state information (CSI)-based HAR task. To capture the dynamics of human activit...
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| Veröffentlicht in: | IEEE/CAA journal of automatica sinica 2023-06, Vol.10 (6), p.1-3 |
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| creator | Shang, Miao Hong, Xiaopeng |
| description | Dear Editor, Human activity recognition (HAR) using WiFi signals has been a significant task due to its potential applications in for example, healthcare services and smart homes. This letter deals with the WiFi channel state information (CSI)-based HAR task. To capture the dynamics of human activities well from CSI without using a huge number of training samples, we propose a recurrent model of convolution blocks and transformer encoders. Firstly, the model utilizes the convolution blocks to capture local variation and the self-attention mechanism in transformer encoders to characterize long-range dependencies. Secondly and more importantly, the recurrent architecture models the context information well within CSI signals and allows the network to deepen without scale increase, making it particularly suited to learning from a small amount of CSI samples. |
| doi_str_mv | 10.1109/JAS.2023.123291 |
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This letter deals with the WiFi channel state information (CSI)-based HAR task. To capture the dynamics of human activities well from CSI without using a huge number of training samples, we propose a recurrent model of convolution blocks and transformer encoders. Firstly, the model utilizes the convolution blocks to capture local variation and the self-attention mechanism in transformer encoders to characterize long-range dependencies. Secondly and more importantly, the recurrent architecture models the context information well within CSI signals and allows the network to deepen without scale increase, making it particularly suited to learning from a small amount of CSI samples.</description><identifier>ISSN: 2329-9266</identifier><identifier>EISSN: 2329-9274</identifier><identifier>DOI: 10.1109/JAS.2023.123291</identifier><identifier>CODEN: IJASJC</identifier><language>eng</language><publisher>Piscataway: Chinese Association of Automation (CAA)</publisher><subject>Coders ; Computer architecture ; Convolution ; Feature extraction ; Human activity recognition ; Smart buildings ; Task analysis ; Training ; Transformers ; Wireless fidelity</subject><ispartof>IEEE/CAA journal of automatica sinica, 2023-06, Vol.10 (6), p.1-3</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-6353ee29d47f45bffcaeaf56650da71f25a892753a606d2f01ea85c1fd8f07c23</citedby><cites>FETCH-LOGICAL-c325t-6353ee29d47f45bffcaeaf56650da71f25a892753a606d2f01ea85c1fd8f07c23</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/zdhxb-ywb/zdhxb-ywb.jpg</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10084426$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10084426$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Shang, Miao</creatorcontrib><creatorcontrib>Hong, Xiaopeng</creatorcontrib><title>Recurrent ConFormer for WiFi activity recognition</title><title>IEEE/CAA journal of automatica sinica</title><addtitle>JAS</addtitle><description>Dear Editor, Human activity recognition (HAR) using WiFi signals has been a significant task due to its potential applications in for example, healthcare services and smart homes. This letter deals with the WiFi channel state information (CSI)-based HAR task. To capture the dynamics of human activities well from CSI without using a huge number of training samples, we propose a recurrent model of convolution blocks and transformer encoders. Firstly, the model utilizes the convolution blocks to capture local variation and the self-attention mechanism in transformer encoders to characterize long-range dependencies. Secondly and more importantly, the recurrent architecture models the context information well within CSI signals and allows the network to deepen without scale increase, making it particularly suited to learning from a small amount of CSI samples.</description><subject>Coders</subject><subject>Computer architecture</subject><subject>Convolution</subject><subject>Feature extraction</subject><subject>Human activity recognition</subject><subject>Smart buildings</subject><subject>Task analysis</subject><subject>Training</subject><subject>Transformers</subject><subject>Wireless fidelity</subject><issn>2329-9266</issn><issn>2329-9274</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1LAzEQxYMoWGrPXjwseBO2nUw22d1jKdYPBMEPPIY0m9QUm9Ts1lr_endZUU9vDr_3ZuYRckphTCmUk9vp4xgB2Zgiw5IekEGnaYl5dvg7C3FMRnW9AgCKPBdlNiD0wehtjMY3ySz4eYhrExMbYvLi5i5RunEfrtkn0eiw9K5xwZ-QI6veajP60SF5nl8-za7Tu_urm9n0LtUMeZMKxpkxWFZZbjO-sFYroywXgkOlcmqRq6I9jzMlQFRogRpVcE1tVVjINbIhuehzd8pb5ZdyFbbRtxvlV_X6uZD73aL7GET7TAuf9_AmhvetqZs_GgukIHIsypaa9JSOoa6jsXIT3VrFvaQgux5l26PsUmXfY-s46x3OGPOPhiLLULBvR0ps0g</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Shang, Miao</creator><creator>Hong, Xiaopeng</creator><general>Chinese Association of Automation (CAA)</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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This letter deals with the WiFi channel state information (CSI)-based HAR task. To capture the dynamics of human activities well from CSI without using a huge number of training samples, we propose a recurrent model of convolution blocks and transformer encoders. Firstly, the model utilizes the convolution blocks to capture local variation and the self-attention mechanism in transformer encoders to characterize long-range dependencies. Secondly and more importantly, the recurrent architecture models the context information well within CSI signals and allows the network to deepen without scale increase, making it particularly suited to learning from a small amount of CSI samples.</abstract><cop>Piscataway</cop><pub>Chinese Association of Automation (CAA)</pub><doi>10.1109/JAS.2023.123291</doi><tpages>3</tpages></addata></record> |
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| subjects | Coders Computer architecture Convolution Feature extraction Human activity recognition Smart buildings Task analysis Training Transformers Wireless fidelity |
| title | Recurrent ConFormer for WiFi activity recognition |
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