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作 者:Lingsheng Li Weiqing Bai Chong Han
机构地区:[1]College of Computer Engineering,Jinling Institute of Technology,Nanjing,211169,China [2]College of Computer,Nanjing University of Posts and Telecommunications,Nanjing,210003,China
出 处:《Computers, Materials & Continua》2024年第10期1613-1640,共28页计算机、材料和连续体(英文)
基 金:supported by the National Natural Science Foundation of China under grant no.62272242.
摘 要:Gestures are one of the most natural and intuitive approach for human-computer interaction.Compared with traditional camera-based or wearable sensors-based solutions,gesture recognition using the millimeter wave radar has attracted growing attention for its characteristics of contact-free,privacy-preserving and less environmentdependence.Although there have been many recent studies on hand gesture recognition,the existing hand gesture recognition methods still have recognition accuracy and generalization ability shortcomings in shortrange applications.In this paper,we present a hand gesture recognition method named multiscale feature fusion(MSFF)to accurately identify micro hand gestures.In MSFF,not only the overall action recognition of the palm but also the subtle movements of the fingers are taken into account.Specifically,we adopt hand gesture multiangle Doppler-time and gesture trajectory range-angle map multi-feature fusion to comprehensively extract hand gesture features and fuse high-level deep neural networks to make it pay more attention to subtle finger movements.We evaluate the proposed method using data collected from 10 users and our proposed solution achieves an average recognition accuracy of 99.7%.Extensive experiments on a public mmWave gesture dataset demonstrate the superior effectiveness of the proposed system.
关 键 词:Gesture recognition millimeter-wave(mmWave)radar radio frequency(RF)sensing human-computer interaction multiscale feature fusion
分 类 号:TP391.41[自动化与计算机技术—计算机应用技术]
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