Clustering-Driven DGS-Based Micro-Doppler Feature Extrac-tion for Automatic Dynamic Hand Gesture Recognition

We propose in this work a dynamic group sparsity (DGS) based time-frequency feature extraction method for dynamic hand gesture recognition (HGR) using millimeter-wave radar sensors. Previous study has pointed out that micro-Doppler signatures of hand gestures exhibit obvious sparseness in time-frequency domain. We introduce the structured prior when modeling the micro-Doppler signatures in this work to further enhance the features of hand gestures. The time-frequency distributions of dynamic hand gestures are first modeled using a dynamic group sparse model. A DGS-Subspace Pursuit (DGS-SP) algorithm is then utilized to extract the corresponding features. Finally, the support vector machine (SVM) classifier is employed to realize the dynamic HGR based on the extracted group sparse micro-Doppler features. The effectiveness of the proposed method is verified using onsite experiments. The results demonstrate that the structured feature is beneficial to improve the accuracy of dynamic hand gesture recognition.