FMCW Radar-Based Real-Time Hand Gesture Recognition System Capable of Out-of-Distribution Detection

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dc.contributor.authorChoi, Jae-Wooko
dc.contributor.authorPark, Chan-Wooko
dc.contributor.authorKim, Jong-Hwanko
dc.date.accessioned2022-09-06T02:01:46Z-
dc.date.available2022-09-06T02:01:46Z-
dc.date.created2022-09-06-
dc.date.created2022-09-06-
dc.date.created2022-09-06-
dc.date.issued2022-
dc.identifier.citationIEEE ACCESS, v.10, pp.87425 - 87434-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10203/298362-
dc.description.abstractReal-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.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleFMCW Radar-Based Real-Time Hand Gesture Recognition System Capable of Out-of-Distribution Detection-
dc.typeArticle-
dc.identifier.wosid000845005700001-
dc.identifier.scopusid2-s2.0-85137579799-
dc.type.rimsART-
dc.citation.volume10-
dc.citation.beginningpage87425-
dc.citation.endingpage87434-
dc.citation.publicationnameIEEE ACCESS-
dc.identifier.doi10.1109/ACCESS.2022.3200757-
dc.contributor.localauthorKim, Jong-Hwan-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorDoppler radar-
dc.subject.keywordAuthorHuman computer interaction-
dc.subject.keywordAuthorGesture recognition-
dc.subject.keywordAuthorReal-time systems-
dc.subject.keywordAuthorTransformers-
dc.subject.keywordAuthorDoppler effect-
dc.subject.keywordAuthorReliability-
dc.subject.keywordAuthorEncoding-
dc.subject.keywordAuthorFMCW radar-
dc.subject.keywordAuthorhand gesture recognition-
dc.subject.keywordAuthorhuman-computer interaction-
dc.subject.keywordAuthorout-of-distribution detection-
dc.subject.keywordAuthortransformer encoder-based classifier-
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