{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T08:00:31Z","timestamp":1707465631799},"reference-count":21,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2017,11,6]],"date-time":"2017-11-06T00:00:00Z","timestamp":1509926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJPCC"],"published-print":{"date-parts":[[2017,11,6]]},"abstract":"\nPurpose<\/jats:title>\nRecently, many researches have been devoted to studying the possibility of using wireless signals of the Wi-Fi networks in human-gesture recognition. They focus on classifying gestures despite who is performing them, and only a few of the previous work make use of the wireless channel state information in identifying humans. This paper aims to recognize different humans and their multiple gestures in an indoor environment.<\/jats:p>\n<\/jats:sec>\n\nDesign\/methodology\/approach<\/jats:title>\nThe authors designed a gesture recognition system that consists of channel state information data collection, preprocessing, features extraction and classification to guess the human and the gesture in the vicinity of a Wi-Fi-enabled device with modified Wi-Fi-device driver to collect the channel state information, and process it in real time.<\/jats:p>\n<\/jats:sec>\n\nFindings<\/jats:title>\nThe proposed system proved to work well for different humans and different gestures with an accuracy that ranges from 87 per cent for multiple humans and multiple gestures to 98 per cent for individual humans\u2019 gesture recognition.<\/jats:p>\n<\/jats:sec>\n\nOriginality\/value<\/jats:title>\nThis paper used new preprocessing and filtering techniques, proposed new features to be extracted from the data and new classification method that have not been used in this field before.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijpcc-d-17-00005","type":"journal-article","created":{"date-parts":[[2017,12,7]],"date-time":"2017-12-07T09:10:09Z","timestamp":1512637809000},"page":"408-418","source":"Crossref","is-referenced-by-count":4,"title":["Towards ubiquitous human gestures recognition using wireless networks"],"prefix":"10.1108","volume":"13","author":[{"given":"Mustafa S.","family":"Aljumaily","sequence":"first","affiliation":[]},{"given":"Ghaida A.","family":"Al-Suhail","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"issue":"4","key":"key2020120418434979400_ref001","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/2534169.2486039","article-title":"See through walls with WiFi!","volume":"43","year":"2013","journal-title":"Acm Sigcomm Computer Communication Review"},{"key":"key2020120418434979400_ref002","first-page":"90","article-title":"Keystroke recognition using wifi 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networks","volume":"16","year":"2017","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"key2020120418434979400_ref017","first-page":"361","article-title":"Electronic frog eye: counting crowd using wifi","year":"2014"},{"issue":"2","key":"key2020120418434979400_ref018","first-page":"25","article-title":"From RSSI to CSI: indoor localization via channel response","volume":"46","year":"2013","journal-title":"ACM Computing Surveys ( Surveys)"},{"key":"key2020120418434979400_ref019","article-title":"Device-free activity identification using fine-grained wifi signatures","year":"2016"},{"key":"key2020120418434979400_ref020","first-page":"75","article-title":"Wifi-id: human identification using wifi signal","year":"2016"},{"key":"key2020120418434979400_ref021","first-page":"3057","article-title":"Towards omnidirectional passive human detection","year":"2013"}],"container-title":["International Journal of Pervasive Computing and 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