{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:55:15Z","timestamp":1774630515569,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,5,20]],"date-time":"2019-05-20T00:00:00Z","timestamp":1558310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In this paper we present an approach towards real-time hand gesture recognition using the Kinect sensor, investigating several machine learning techniques. We propose a novel approach for feature extraction, using measurements on joints of the extracted skeletons. The proposed features extract angles and displacements of skeleton joints, as the latter move into a 3D space. We define a set of gestures and construct a real-life data set. We train gesture classifiers under the assumptions that they shall be applied and evaluated to both known and unknown users. Experimental results with 11 classification approaches prove the effectiveness and the potential of our approach both with the proposed dataset and also compared to state-of-the-art research works.<\/jats:p>","DOI":"10.3390\/a12050108","type":"journal-article","created":{"date-parts":[[2019,5,20]],"date-time":"2019-05-20T11:05:07Z","timestamp":1558350307000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Real-Time Arm Gesture Recognition Using 3D Skeleton Joint Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Georgios","family":"Paraskevopoulos","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, National Technical University of Athens, Iroon Polytechneiou 9, 157 73 Zografou, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evaggelos","family":"Spyrou","sequence":"additional","affiliation":[{"name":"Institute of Informatics and Telecommunications, NCSR Demokritos, Neapoleos 10, 153 41 Ag. Paraskevi, Greece"},{"name":"Department of Computer Science and Telecommunications, University of Thessaly, 3rd km Old National Rd. Lamia-Athens, 351 00 Lamia, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitrios","family":"Sgouropoulos","sequence":"additional","affiliation":[{"name":"Institute of Informatics and Telecommunications, NCSR Demokritos, Neapoleos 10, 153 41 Ag. Paraskevi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Theodoros","family":"Giannakopoulos","sequence":"additional","affiliation":[{"name":"Institute of Informatics and Telecommunications, NCSR Demokritos, Neapoleos 10, 153 41 Ag. Paraskevi, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6916-3129","authenticated-orcid":false,"given":"Phivos","family":"Mylonas","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, Platia Tsirigoti 7, 491 00 Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bhattacharya, S., Czejdo, B., and Perez, N. (December, January 30). Gesture classification with machine learning using kinect sensor data. Proceedings of the 2012 Third International Conference on Emerging Applications of Information Technology, Kolkata, India.","DOI":"10.1109\/EAIT.2012.6407958"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lai, K., Konrad, J., and Ishwar, P. (2012, January 22\u201324). A gesture-driven computer interface using kinect. 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