{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T20:14:16Z","timestamp":1777493656717,"version":"3.51.4"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T00:00:00Z","timestamp":1565308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2019,8,31]]},"abstract":"<jats:p>Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this article, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier, and activity recognition also becomes harder. Our extensive experiments show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.<\/jats:p>","DOI":"10.1145\/3338026","type":"journal-article","created":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T12:22:28Z","timestamp":1565353348000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":29,"title":["From Real to Complex"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0781-9655","authenticated-orcid":false,"given":"Bo","family":"Wei","sequence":"first","affiliation":[{"name":"Northumbria University, Tyne and Wear, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Hu","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingrui","family":"Yang","sequence":"additional","affiliation":[{"name":"Cleveland Clinic, Cleveland, Ohio, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun Tung","family":"Chou","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,8,9]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the NSDI\u201914","author":"Adib Fadel","unstructured":"Fadel Adib , Zach Kabelac , Dina Katabi , and Robert C. Miller . 2014. 3D tracking via body radio reflections . In Proceedings of the NSDI\u201914 . Fadel Adib, Zach Kabelac, Dina Katabi, and Robert C. Miller. 2014. 3D tracking via body radio reflections. In Proceedings of the NSDI\u201914."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2486001.2486039"},{"key":"e_1_2_1_3_1","volume-title":"Intille","author":"Bao Ling","year":"2004","unstructured":"Ling Bao and Stephen S . Intille . 2004 . Activity recognition from user-annotated acceleration data. In Pervasive Computing. Springer , 1--17. Ling Bao and Stephen S. Intille. 2004. Activity recognition from user-annotated acceleration data. In Pervasive Computing. Springer, 1--17."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/946242.946286"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1925861.1925870"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517351.2517359"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1896300.1896359"},{"key":"e_1_2_1_8_1","unstructured":"Jawbone. 2014. UP. Retrieved from: https:\/\/jawbone.com\/up.  Jawbone. 2014. UP. Retrieved from: https:\/\/jawbone.com\/up."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MASS.2012.6502524"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2070942.2070968"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the NSDI\u201914","author":"Kellogg Bryce","year":"2014","unstructured":"Bryce Kellogg , Vamsi Talla , and Shyamnath Gollakota . 2014 . Bringing gesture recognition to all devices . In Proceedings of the NSDI\u201914 . Bryce Kellogg, Vamsi Talla, and Shyamnath Gollakota. 2014. Bringing gesture recognition to all devices. In Proceedings of the NSDI\u201914."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964897.1964918"},{"key":"e_1_2_1_13_1","unstructured":"Leap Motion Inc. 2014. Leap Motion. Retrieved from: https:\/\/www.leapmotion.com\/.  Leap Motion Inc. 2014. Leap Motion. Retrieved from: https:\/\/www.leapmotion.com\/."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790110"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191755"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.66"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2632048.2632095"},{"key":"e_1_2_1_18_1","unstructured":"Microsoft. 2014. Kinect. Retrieved from: http:\/\/www.microsoft.com\/en-us\/kinectforwindows\/.  Microsoft. 2014. Kinect. Retrieved from: http:\/\/www.microsoft.com\/en-us\/kinectforwindows\/."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185677.2185680"},{"key":"e_1_2_1_20_1","volume-title":"Niklas Wirstrom, and Thiemo Voigt.","author":"Misra Prasant Kumar","year":"2014","unstructured":"Prasant Kumar Misra , Wen Hu , Yuzhe Jin , Jie Liu , Amanda Souza de Paula , Niklas Wirstrom, and Thiemo Voigt. 2014 . Energy efficient GPS acquisition with sparse-GPS. In Proceedings of the IPSN\u201914. IEEE Press , 155--166. Prasant Kumar Misra, Wen Hu, Yuzhe Jin, Jie Liu, Amanda Souza de Paula, Niklas Wirstrom, and Thiemo Voigt. 2014. Energy efficient GPS acquisition with sparse-GPS. In Proceedings of the IPSN\u201914. IEEE Press, 155--166."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMI.2002.1166960"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2500423.2500436"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3025453.3025678"},{"key":"e_1_2_1_24_1","first-page":"1541","article-title":"Activity recognition from accelerometer data","volume":"5","author":"Ravi Nishkam","year":"2005","unstructured":"Nishkam Ravi , Nikhil Dandekar , Preetham Mysore , and Michael L. Littman . 2005 . Activity recognition from accelerometer data . In Proceedings of the AAAI , Vol. 5. 1541 -- 1546 . Nishkam Ravi, Nikhil Dandekar, Preetham Mysore, and Michael L. Littman. 2005. Activity recognition from accelerometer data. In Proceedings of the AAAI, Vol. 5. 1541--1546.","journal-title":"Proceedings of the AAAI"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2010.2051027"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2462456.2464463"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2307636.2307654"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2426656.2426686"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2015.2418775"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/2602339.2602366"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2593919"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2013.28"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2009.2036999"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1137\/080714488"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1137\/100785028"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2639108.2639112"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2971648.2971670"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2017.2679658"},{"key":"e_1_2_1_39_1","first-page":"763","article-title":"CSI-based fingerprinting for indoor localization: A deep learning approach","volume":"66","author":"Wang Xuyu","year":"2017","unstructured":"Xuyu Wang , Lingjun Gao , Shiwen Mao , and Santosh Pandey . 2017 . CSI-based fingerprinting for indoor localization: A deep learning approach . IEEE TVT 66 , 1 (2017), 763 -- 776 . Xuyu Wang, Lingjun Gao, Shiwen Mao, and Santosh Pandey. 2017. CSI-based fingerprinting for indoor localization: A deep learning approach. IEEE TVT 66, 1 (2017), 763--776.","journal-title":"IEEE TVT"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.206"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2639108.2639143"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2737095.2737117"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2737095.2737118"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517351.2517357"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2009.174"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.79"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185677.2185679"},{"key":"e_1_2_1_48_1","unstructured":"Xiaomi. 2014. Mi Band. Retrieved from: http:\/\/www.mi.com\/shouhuan.  Xiaomi. 2014. Mi Band. Retrieved from: http:\/\/www.mi.com\/shouhuan."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461381.2461394"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185677.2185734"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2426656.2426697"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/2959355.2959357"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2543581.2543592"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370269"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/1287853.1287880"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.5555\/2959355.2959359"},{"key":"e_1_2_1_57_1","volume-title":"Kanhere","author":"Zhang Jin","year":"2016","unstructured":"Jin Zhang , Bo Wei , Wen Hu , and Salil S . Kanhere . 2016 . WiFi-ID: Human identification using WiFi signal. In Proceedings of the DCOSS. IEEE , 75--82. Jin Zhang, Bo Wei, Wen Hu, and Salil S. Kanhere. 2016. WiFi-ID: Human identification using WiFi signal. In Proceedings of the DCOSS. IEEE, 75--82."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/2973750.2973762"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.296"},{"key":"e_1_2_1_60_1","volume-title":"Proceedings of the SECON\u201911","author":"Zhao Yang","unstructured":"Yang Zhao and N. Patwari . 2011. Noise reduction for variance-based device-free localization and tracking . In Proceedings of the SECON\u201911 . 179--187. Yang Zhao and N. Patwari. 2011. Noise reduction for variance-based device-free localization and tracking. In Proceedings of the SECON\u201911. 179--187."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461381.2461410"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2017.2752367"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2013.274"}],"container-title":["ACM Transactions on Sensor Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3338026","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3338026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:25:42Z","timestamp":1750206342000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3338026"}},"subtitle":["Enhancing Radio-based Activity Recognition Using Complex-Valued CSI"],"short-title":[],"issued":{"date-parts":[[2019,8,9]]},"references-count":63,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2019,8,31]]}},"alternative-id":["10.1145\/3338026"],"URL":"https:\/\/doi.org\/10.1145\/3338026","relation":{},"ISSN":["1550-4859","1550-4867"],"issn-type":[{"value":"1550-4859","type":"print"},{"value":"1550-4867","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,9]]},"assertion":[{"value":"2018-07-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-05-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-08-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}