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Radar-Based Gesture Recognition Using a Variational Autoencoder With Deep Statistical Metric Learning
Publikationstyp
Journal Article
Publikationsdatum
2022-11-01
Sprache
English
Institut
Volume
70
Issue
11
Start Page
5051
End Page
5062
Citation
IEEE Transactions on Microwave Theory and Techniques 70 (11): 5051-5052 (2022-11-01)
Publisher DOI
Scopus ID
2-s2.0-85137888860
Radar-based gesture recognition systems are a promising concept for new human–machine interfaces. However, the systems are sensitive to user-dependent gesture patterns, sensor noise characteristics, background environments, and unknown gestures or background motions. To overcome such challenges, we propose a variational autoencoder architecture-based deep metric learning, which is optimized using a novel loss function combining a statistical distance triplet loss and the center loss. By learning the statistical distance between distributions, the proposed solution, compared with triplet loss, is able to better learn the nonlinear characteristics in the data, is less sensitivity to training strategy, and is capable of creating close knit embedding clusters. Moreover, it is experimentally demonstrated that the proposed gesture recognition system using radar spectrograms has improved classification accuracy and random gesture rejection accuracy compared with the state-of-the-art deep metric learning approaches. Furthermore, we proof the real-world capability of the system by implementing a real-time demonstrator, which allows playing “Tetris” with hand gestures.
Schlagworte
Chirp
Doppler radar
Frequency-modulated continuous wave (FMCW) radar
Gesture recognition
hand gesture recognition
Measurement
microwave motion sensors
open-set classification
Radar antennas
Radar imaging
radar systems
Spectrogram