Hand gesture recognition based on radar micro-doppler signature envelopes

We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler (MD) signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies that are generated during each gesture act. We detect the positive and negative frequency envelopes of MD separately, and form a feature vector of their augmentation. We use the k-nearest neighbor (kNN) classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance (L2), so as not to diminish small but critical envelope values. It is shown that this method outperforms both low-dimension representation techniques based on principal component analysis (PCA) and sparse reconstruction using Gaussian-windowed Fourier dictionary, and can achieve very high classification rates.