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Hand and arm gesture recognition using the radio frequency (RF) sensingmodality proves valuable in manmachine interface and smart environment. In thispaper, we use curve matching techniques for measuring the similarity of themaximum instantaneous Doppler frequencies corresponding to different armgestures. In particular, we apply both Frechet and dynamic time warping (DTW)distances that, unlike the Euclidean (L2) and Manhattan (L1) distances, takeinto account both the location and the order of the points for rendering twocurves similar or dissimilar. It is shown that improved arm gestureclassification can be achieved by using the DTW method, in lieu of L2 and L1distances, under the nearest neighbor (NN) classifier.

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