@inproceedings{47bccf124e694749bf8d423993d87e2a,
title = "Recognition of dynamic hand gesture based on mm-wave FMCW radar micro-Doppler signatures",
abstract = "Radar-based sensors provide an attractive choice for hand gesture recognition (HGR). The very challenging problems in radar-based HGR are radar echo data preprocessing and recognition accuracy. In this paper, we propose a convolutional neural network (CNN) for dynamic HGR based on a millimeter-wave Frequency Modulated Continuous Wave (FMCW) radar which operates at 77GHz. Six different dynamic hand gestures are designed and the time-frequency analysis of micro-Doppler signatures are adopted as the input to CNN. The measured data of the dynamic hand gestures are collected in different experimental scenarios. The recognition accuracy of the six gestures based on the measured data reached 95.2\%. The experimental results demonstrate that the proposed method is effective in the measured data and the micro-Doppler signature is effective for dynamic HGR.",
keywords = "Convolutional neural network, FMCW radar, Hand gesture recognition, Micro-Doppler signatures, Millimeter wave radar",
author = "Wen Jiang and Yihui Ren and Ying Liu and Ziao Wang and Xinghua Wang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 ; Conference date: 06-06-2021 Through 11-06-2021",
year = "2021",
doi = "10.1109/ICASSP39728.2021.9414837",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4905--4909",
booktitle = "2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Proceedings",
address = "United States",
}