Hand Gesture Recognition via Radar Sensors and Convolutional Neural Networks

In this communication, a low-cost radar-sensor-based apparatus for contactless hand gesture recognition via Doppler signature analysis is proposed. The raw reflected signal, after some pre-processing, is analysed via its time-frequency representation, known as spectrogram. This information is then e...

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Bibliographic Details
Main Authors: Franceschini, S., Ambrosanio, M., Vitale, S., Baselice, F., Gifuni, A., Grassini, G., Pascazio, V.
Format: Conference Proceeding
Language:English
Subjects:
Convolutional neural networks
Doppler effect
hand-gesture recognition
Radar
Radar sensors
radar signal processing
Sensors
Spectrogram
Time-frequency analysis
Training
ultrasonic active sensing
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Description
Summary:In this communication, a low-cost radar-sensor-based apparatus for contactless hand gesture recognition via Doppler signature analysis is proposed. The raw reflected signal, after some pre-processing, is analysed via its time-frequency representation, known as spectrogram. This information is then exploited to train a convolutional neural network (CNN) to perform the classification step. The whole procedure was tested on an in-house experimental data set composed of four different hand gestures, showing good performance and reaching an accuracy of approximately 97%. Finally, the classification performance was tested also in a cluttered environment which includes the presence of a strong echo close to the target.
ISSN:2375-5318
DOI:10.1109/RadarConf2043947.2020.9266565