Radar-Based Hand Gesture Recognition Using Spiking Neural Networks

Ing Jyh Tsang, Federico Corradi, Manolis Sifalakis, Werner Van Leekwijck, Steven Latré
2021 Electronics  
We propose a spiking neural network (SNN) approach for radar-based hand gesture recognition (HGR), using frequency modulated continuous wave (FMCW) millimeter-wave radar. After pre-processing the range-Doppler or micro-Doppler radar signal, we use a signal-to-spike conversion scheme that encodes radar Doppler maps into spike trains. The spike trains are fed into a spiking recurrent neural network, a liquid state machine (LSM). The readout spike signal from the SNN is then used as input for
more » ... rent classifiers for comparison, including logistic regression, random forest, and support vector machine (SVM). Using liquid state machines of less than 1000 neurons, we achieve better than state-of-the-art results on two publicly available reference datasets, reaching over 98% accuracy on 10-fold cross-validation for both data sets.
doi:10.3390/electronics10121405 fatcat:vasb26pc7zdpzj23cqdx6f5zzy