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Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

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Dynamic hand gesture recognition is of great importance in human–computer interaction. In this study, the authors investigate the effect of sparsity-driven time–frequency analysis on hand gesture classification. The time–frequency spectrogram is first obtained by sparsity-driven time–frequency analysis. Then three empirical micro-Doppler features are extracted from the time–frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time–frequency analysis techniques, sparsity-driven time–frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification.

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