Dynamic Hand Gesture Recognition Based on Micro-Doppler Radar Signatures Using Hidden Gauss–Markov Models

Dynamic hand gesture recognition using the microwave or millimeter-wave radar sensors has become a typical technology for many human-computer interaction (HCI) applications. In this letter, a novel method is proposed for dynamic hand gesture recognition based on micro-Doppler radar signatures. The short-time Fourier transform is carried out on the raw data to obtain the time-frequency spectrogram. The time-frequency spectrograms associated with the same dynamic hand gesture are modeled by a hidden Gauss-Markov model (HGMM), and the testing gesture is recognized by the maximum likelihood criterion. Experimental results with real radar data demonstrate that the proposed method has a strong generalization ability for radar gesture recognition in the cases of low signal-to-noise ratio (SNR) and unknown users.