DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Jae-Woo | ko |
dc.contributor.author | Park, Chan-Woo | ko |
dc.contributor.author | Kim, Jong-Hwan | ko |
dc.date.accessioned | 2022-09-06T02:01:46Z | - |
dc.date.available | 2022-09-06T02:01:46Z | - |
dc.date.created | 2022-09-06 | - |
dc.date.created | 2022-09-06 | - |
dc.date.created | 2022-09-06 | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | IEEE ACCESS, v.10, pp.87425 - 87434 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298362 | - |
dc.description.abstract | Real-time hand gesture recognition plays a vital role in human-computer interaction (HCI). Recent radar-based hand gesture recognition methods have focused on achieving high classification accuracy using deep neural network (DNN)-based classifiers. However, the hand gesture recognition system should not only classify the gestures accurately but also detect out-of-distribution (OOD) samples to be used in real-world HCI scenarios with high reliability. Recognition systems without OOD detection capability misclassify unintended gestures in silence, especially in real-time scenarios. To tackle this problem, we propose a real-time hand gesture recognition system that can simultaneously classify hand gestures and detect OOD samples by using a Frequency Modulated Continuous Wave (FMCW) radar sensor. First, we design radar data processing technique and Transformer encoder-based classifier to achieve high classification accuracy. Second, the relative Mahalanobis distance (RMD)-based OOD detection method is adopted to increase the reliability of the proposed system. Finally, one in-distribution dataset and two OOD datasets are collected to verify the proposed system. The proposed system achieves a classification accuracy of 93.95% on the in-distribution dataset. We conduct the OOD detection experiments with two OOD datasets for which the proposed system reports AUROC values of 92.96% and 92.84%, respectively. Furthermore, the feasibility of the proposed system is certified through a real-time experimental demonstration. | - |
dc.language | English | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | FMCW Radar-Based Real-Time Hand Gesture Recognition System Capable of Out-of-Distribution Detection | - |
dc.type | Article | - |
dc.identifier.wosid | 000845005700001 | - |
dc.identifier.scopusid | 2-s2.0-85137579799 | - |
dc.type.rims | ART | - |
dc.citation.volume | 10 | - |
dc.citation.beginningpage | 87425 | - |
dc.citation.endingpage | 87434 | - |
dc.citation.publicationname | IEEE ACCESS | - |
dc.identifier.doi | 10.1109/ACCESS.2022.3200757 | - |
dc.contributor.localauthor | Kim, Jong-Hwan | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Doppler radar | - |
dc.subject.keywordAuthor | Human computer interaction | - |
dc.subject.keywordAuthor | Gesture recognition | - |
dc.subject.keywordAuthor | Real-time systems | - |
dc.subject.keywordAuthor | Transformers | - |
dc.subject.keywordAuthor | Doppler effect | - |
dc.subject.keywordAuthor | Reliability | - |
dc.subject.keywordAuthor | Encoding | - |
dc.subject.keywordAuthor | FMCW radar | - |
dc.subject.keywordAuthor | hand gesture recognition | - |
dc.subject.keywordAuthor | human-computer interaction | - |
dc.subject.keywordAuthor | out-of-distribution detection | - |
dc.subject.keywordAuthor | transformer encoder-based classifier | - |
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