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Hand gesture recognition based on range Doppler-angle trajectory and LSTM network using an MIMO radar
Author(s): Xinbo Zheng; Zhaocheng Yang; Kaixuan He; Haifan Liu
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Paper Abstract

Touchless hand gesture recognition is of great importance for human-computer interaction (HCI). In this paper, we present a hand gesture recognition approach based on range-Doppler-angle trajectory and the long short-term memory (LSTM) network with a 77GHz frequency modulated continuous wave (FMCW) multiple-input-multiple-output (MIMO) radar. Firstly, the hand gesture fast-time-slow-time-antenna 3 dimension (3D) data are collected by the FMCW MIMO radar. Additionally, by performing the discretize Fourier transform (DFT) to the fast-time and slow-time, respectively, we obtain the range-profile and Doppler-profile. Then, by using the multiple signal classification (MUSIC) approach, we estimate the angle-profile of the hand gestures. To smooth and eliminate the noise effects, we apply the Kalman filtering to the estimated range-profile, Doppler-profile and angle-profile, respectively, and obtain the range-Doppler-angle trajectory signature. After that, by exploiting the temporal and spatial correlations, we construct a LSTM network for the hand gesture recognition. Experiments with 6 hand gestures are conducted and show that the proposed approach can recognize 6 hand gestures with an average accuracy over 97%.

Paper Details

Date Published: 31 December 2019
PDF: 11 pages
Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 113840P (31 December 2019); doi: 10.1117/12.2558317
Show Author Affiliations
Xinbo Zheng, Shenzhen Univ. (China)
Zhaocheng Yang, Shenzhen Univ. (China)
Kaixuan He, Shenzhen Univ. (China)
Haifan Liu, Shenzhen Univ. (China)

Published in SPIE Proceedings Vol. 11384:
Eleventh International Conference on Signal Processing Systems
Kezhi Mao, Editor(s)

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