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.
Original language | English |
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Article number | 2314 |
Journal | Applied Sciences (Switzerland) |
Volume | 9 |
Issue number | 11 |
DOIs | |
Publication status | Published - Jun 1 2019 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2019 by the authors.
ASJC Scopus Subject Areas
- General Materials Science
- Instrumentation
- General Engineering
- Process Chemistry and Technology
- Computer Science Applications
- Fluid Flow and Transfer Processes
Keywords
- Convolutional neural network
- Finger gesture recognition
- Themicro-Doppler effect
- Ultrasonic