{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T07:45:04Z","timestamp":1769845504532,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T00:00:00Z","timestamp":1562198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 \u00d7 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.<\/jats:p>","DOI":"10.3390\/s19132959","type":"journal-article","created":{"date-parts":[[2019,7,4]],"date-time":"2019-07-04T11:13:18Z","timestamp":1562238798000},"page":"2959","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices"],"prefix":"10.3390","volume":"19","author":[{"given":"Hasmath","family":"Farhana Thariq Ahmed","sequence":"first","affiliation":[{"name":"School of Engineering, Taylor\u2019s University, 1, Jalan Taylor\u2019s, Subang Jaya, Selangor 47500, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hafisoh","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Engineering, Taylor\u2019s University, 1, Jalan Taylor\u2019s, Subang Jaya, Selangor 47500, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7877-8766","authenticated-orcid":false,"given":"Swee King","family":"Phang","sequence":"additional","affiliation":[{"name":"School of Engineering, Taylor\u2019s University, 1, Jalan Taylor\u2019s, Subang Jaya, Selangor 47500, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chockalingam Aravind","family":"Vaithilingam","sequence":"additional","affiliation":[{"name":"School of Engineering, Taylor\u2019s University, 1, Jalan Taylor\u2019s, Subang Jaya, Selangor 47500, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7827-1527","authenticated-orcid":false,"given":"Houda","family":"Harkat","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Technologies, University of Sidi Mohamed Ben Abdellah, Route Imouzzer Fez, BP 2626, Fes 30000, Morocco"},{"name":"Faculty of Science and Technology, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7919-7229","authenticated-orcid":false,"given":"Kulasekharan","family":"Narasingamurthi","sequence":"additional","affiliation":[{"name":"Simulation Metier, Valeo India Pvt Ltd., 1\/396, Old Mahabalipuram Road, Navallur, Chennai, Tamil Nadu 600130, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.neucom.2018.05.042","article-title":"A novel framework of continuous human-activity recognition using Kinect","volume":"311","author":"Saini","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_2","unstructured":"Kellogg, B., Talla, V., and Gollakota, S. 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