Hand Gesture Recognition in Range-Doppler Images Using Binary Activated Spiking Neural Networks

Many hand gesture recognition systems use radar to sense the motion of the hand due to its independence of lighting and its inherent privacy. As in the case of cameras, complex signal processing chains consisting of classical algorithms and neural network-base approaches are necessary to evaluate the incoming data stream. Especially on mobile devices, the reduction of the total energy consumption of the recognition system is crucial as it would lead to an increased battery life. Spiking neural networks have been shown to consume much less energy than current networks by operating event-driven and using time as the main information carrier. However, practical applications in which they are on par with classical approaches are rare. In this paper we utilize spiking neural networks to perform hand gesture recognition in radar data. We show that the temporal affinity of spiking networks and the possibility to binarize the radar-generated range-Doppler images without large loss of information introduces a promising synergy. Using simple networks consisting of 75 recurrently connected spiking neurons, we are able to reach current state-of-the-art performance on two public datasets. With this approach, gesture recognition systems can operate much more energy-efficient, making spiking neural networks viable alternatives to current solutions.