Electromyography (EMG) data gives information about the electrical activity related to muscles. EMG obtained from arm through sensors helps understand hand gestures. For this work, gesture were taken UCI2019 dataset MYO thalmic armband classied with six dierent machine learning algorithms. Articial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), ...

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 signal-to-spike conversion scheme that encodes Doppler maps into spike trains. The trains are fed recurrent network, liquid state machine (LSM). read...