Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조성호 | - |
dc.date.accessioned | 2022-11-02T05:04:39Z | - |
dc.date.available | 2022-11-02T05:04:39Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.citation | REMOTE SENSING, v. 13, no. 3, article no. 527, page. 1-24 | en_US |
dc.identifier.issn | 2072-4292 | en_US |
dc.identifier.uri | https://www.mdpi.com/2072-4292/13/3/527 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/176218 | - |
dc.description.abstract | Human-Computer Interfaces (HCI) deals with the study of interface between humans and computers. The use of radar and other RF sensors to develop HCI based on Hand Gesture Recognition (HGR) has gained increasing attention over the past decade. Today, devices have built-in radars for recognizing and categorizing hand movements. In this article, we present the first ever review related to HGR using radar sensors. We review the available techniques for multi-domain hand gestures data representation for different signal processing and deep-learning-based HGR algorithms. We classify the radars used for HGR as pulsed and continuous-wave radars, and both the hardware and the algorithmic details of each category is presented in detail. Quantitative and qualitative analysis of ongoing trends related to radar-based HCI, and available radar hardware and algorithms is also presented. At the end, developed devices and applications based on gesture-recognition through radar are discussed. Limitations, future aspects and research directions related to this field are also discussed. | en_US |
dc.description.sponsorship | This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government -MSIT. (2017M3A9E2064626). The authors would also like to thank all the human volunteers for their time and effort in data acquisition. | en_US |
dc.language | en | en_US |
dc.publisher | MDPI | en_US |
dc.subject | hand-gesture recognition; pulsed radar; continuous-wave radars; human–computer interfaces; deep-learning for radar signals | en_US |
dc.title | Hand Gestures Recognition Using Radar Sensors for Human-Computer-Interaction: A Review | en_US |
dc.type | Article | en_US |
dc.relation.no | 3 | - |
dc.relation.volume | 13 | - |
dc.identifier.doi | 10.3390/rs13030527 | en_US |
dc.relation.page | 1-24 | - |
dc.relation.journal | REMOTE SENSING | - |
dc.contributor.googleauthor | Ahmed, Shahzad | - |
dc.contributor.googleauthor | Kallu, Karam Dad | - |
dc.contributor.googleauthor | Ahmed, Sarfaraz | - |
dc.contributor.googleauthor | Cho, Sung Ho | - |
dc.relation.code | 2021007541 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | SCHOOL OF ELECTRONIC ENGINEERING | - |
dc.identifier.pid | dragon | - |
dc.identifier.researcherID | P-1657-2015 | - |
dc.identifier.orcid | https://orcid.org/0000-0002-2393-1428 | - |
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