Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks
This addresses a problem for human-computer interaction by enabling gesture recognition in low-light environments, though it is incremental as it applies existing methods to a new sensor type.
The paper tackled hand gesture recognition in dark conditions by proposing a Doppler radar-based system using convolutional neural networks, achieving 98% accuracy on four standard gestures.
Hand gesture recognition has long been a hot topic in human computer interaction. Traditional camera-based hand gesture recognition systems cannot work properly under dark circumstances. In this paper, a Doppler Radar based hand gesture recognition system using convolutional neural networks is proposed. A cost-effective Doppler radar sensor with dual receiving channels at 5.8GHz is used to acquire a big database of four standard gestures. The received hand gesture signals are then processed with time-frequency analysis. Convolutional neural networks are used to classify different gestures. Experimental results verify the effectiveness of the system with an accuracy of 98%. Besides, related factors such as recognition distance and gesture scale are investigated.