Evaluation of Spiking Neural Networks for Time Domain-based Radar Hand Gesture Recognition

Radar-based hand gesture recognition is a promising alternative to the camera-based solutions since radar is not impacted by lighting conditions and has no privacy concerns. Energy consumption is a key concern for radar applications on edge devices. Thus, a time-domain-based training approach that avoids the computationally expensive pre-processing fast Fourier transform (FFT) steps and utilizes time-domain radar data has been used. Spiking neural networks (SNNs) are recognized as being lower-power and more energy-efficient than artificial neural networks (ANNs). Therefore, we used the timedomain training approach alongside SNNs to conserve the most energy. This work evaluates several convolutional-based SNNs and their ANN variants to determine the SNNs appropriateness for temporally based datasets and their ability to learn complex spatio-temporal features. All models were trained using only time-domain data and then used to classify ten different gestures recorded by five different people using a 60 GHz frequencymodulated continuous-wave (FMCW) radar sensor. The results indicate the effectiveness of the used time-domain training approach and the ability of SNNs to outperform their ANN counterparts.