Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures

Hand and arm gesture recognition using the radio frequency (RF) sensing modality proves valuable in man-machine interface and smart environment. In this paper, we use curve matching techniques for measuring the similarities and differences of the maximum instantaneous Doppler frequencies corresponding to different arm gestures. In particular, we apply both Fréchet and dynamic time warping (DTW) distances that, unlike the Euclidean (L2) and Manhattan (L1) distances, take into account both the location and the order of the points for rendering two curves similar or dissimilar. It is shown that improved arm gesture classification can be achieved by using the DTW method, in lieu of L2 and L1 distances, under the nearest neighbor (NN) classifier.