Hand Gesture Recognition Using IR-UWB Radar with Spiking Neural Networks

Hand gesture recognition has emerged in recent years as a robust method in non-contact human-computer interfaces, especially in the application scenario of the Internet of Things. This paper proposes a high-accuracy and low-power algorithm for hand gesture recognition. The hand gesture dataset was collected by Integrated Systems Lab at ETH Zurich using a low-cost impulse radio ultra-wideband (IR-UWB) radar. The signals are transformed into spikes sequence by time-event coding and level-crossing sampling. These spike arrays are processed by spiking neural networks (SNNs), which have more biological interpretability and are inherently suitable for processing time-series signals. The algorithm has achieved 95.44% accuracy in 5 hand gestures and 96.60% accuracy in 6 hand gestures. As for power consumption, the classification network operates 350 kFLOPs per data sequence on 5 hand gesture datasets, which is 90x smaller than the previous approach.