| 總結: | With the popularity of small-screen smart mobile devices, gestures as a new type of human–computer interaction are highly demanded. Furthermore, finger gestures are more familiar to people in controlling devices. In this paper, a new method for recognizing finger gestures is proposed. Ultrasound was actively emitted to measure the micro-Doppler effect caused by finger motions and was obtained at high resolution. By micro-Doppler processing, micro-Doppler feature maps of finger gestures were generated. Since the feature map has a similar structure to the single channel color image, a recognition model based on a convolutional neural network was constructed for classification. The optimized recognition model achieved an average accuracy of 96.51% in the experiment.
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