Doppler radar for dynamic hand gesture recognition based on signal image processing

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dc.contributor.authorArthamanolap, Kongphum
dc.contributor.authorGabbualoy, Somprasong
dc.contributor.authorPhasukkit, Pattarapong
dc.date.accessioned2026-08-06T10:25:00Z
dc.date.available2026-08-06T10:25:00Z
dc.date.issued2019-07-01
dc.description.abstractFrom previous researches, Doppler radar was used to detect signal for implement with many applications. Nevertheless, it is difficult to analyze for recognize object. At present, technique of deep learning in terms of signal processing and image processing are using in many researches to classify categories of data. In this paper, signal image was used by deep learning to classify hand gesture by receiving signals from 24GHz transceiver: BGT24MTR11. We transformed the signals to images for 3 categories including Spectrogram, Time domain from original signal and feature MFCC graph. After that those of converted image will be trained by Deep learning for classify the hand gesture types. From the result of this experiment has been shown that signal image can be used to recognize hand gesture and Spectrogram graph makes the highest accuracy as 94%.
dc.identifier.citationProceedings of the 16th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2019, 931-934, 2019
dc.identifier.doi10.1109/ECTI-CON47248.2019.8955217
dc.identifier.other2-s2.0-85078833287
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/9974
dc.sourceProceedings of the 16th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2019
dc.subjectBGT24MTR11
dc.subjectDeep Learning
dc.subjectDoppler radar
dc.subjectHand gesture
dc.subjectMFCC
dc.subjectSignal image
dc.subjectSpectrogram
dc.titleDoppler radar for dynamic hand gesture recognition based on signal image processing
dc.typeConference Paper

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