{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T21:06:53Z","timestamp":1783544813512,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,2]],"date-time":"2022-06-02T00:00:00Z","timestamp":1654128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kyushu Institute of Technology (Kyutech)"},{"name":"National Taiwan University of Science and Technology (Taiwan-Tech)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There have been several studies of hand gesture recognition for human\u2013machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range, velocity, and angle. With these signatures, we can customize our system to different scenarios. We proposed an end-to-end training deep learning model (neural network and long short-term memory), that extracts the transformed radar signals into features and classifies the extracted features into hand gesture labels. In our training data collecting effort, a camera is used only to support labeling hand gesture data. The accuracy of our model can reach 98%.<\/jats:p>","DOI":"10.3390\/s22114260","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T08:01:18Z","timestamp":1654243278000},"page":"4260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Implementing a Hand Gesture Recognition System Based on Range-Doppler Map"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7791-6410","authenticated-orcid":false,"given":"Yu-Chiao","family":"Jhaung","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Ming","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiao","family":"Zha","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7197-9912","authenticated-orcid":false,"given":"Jenq-Shiou","family":"Leu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"K\u00f6ppen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Systems Engineering (CSSE), Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka-shi 820-8502, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-012-9356-9","article-title":"Vision based hand gesture recognition for human computer interaction: A survey","volume":"43","author":"Rautaray","year":"2015","journal-title":"Artif. 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