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.
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Hand Gesture Recognition via Radar Sensors and Convolutional Neural Networks
Semantic Scholar · Engineering · 2020
Abstract
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.