Abstract
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.
Highlights
IntroductionTouchscreen control is used in most mobile devices, such as mobile phones and tablets
Touchscreen control is used in most mobile devices, such as mobile phones and tablets.When a person uses it with a wet hand or gloved hand, the touch will not work well
Training a deep neural network requires a large amount of training data containing enough variations of gestures
Summary
Touchscreen control is used in most mobile devices, such as mobile phones and tablets. When a person uses it with a wet hand or gloved hand, the touch will not work well. With the rapid development of mobile devices, such as small-sized smart watches, it is very inconvenient to control devices on a small screen. As a part of human communication, gestures can be used to express a wide variety of emotions and thoughts. Gestures are usually the second most natural method of interaction between humans and the environment, as well as among humans [1]. Gestures are convenient and have a vast interaction space and super high flexibility, providing excellent interactive experience. Gestures in human–computer interactions have gained greater attention in recent years [2]
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