Human hand motion recognition using Empirical Copula

Programming by Demonstration (PbD) enables robotic hands to learn human manipulation skills through storing motion primitives and recognizing motion types. In this paper, Empirical Copula is introduced to recognize dynamic human hand motions for the first time using the proposed motion template and matching algorithm. The huge computational cost of Empirical Copula is alleviated by the proposed re-sampling processing. The experiments with human hand motions including grasps and in-hand manipulations demonstrate Empirical Copula outperforms the Time Clustering (TC) method, Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in terms of recognition rate. In addition, Empirical Copula is also proved to be able to recognize different motions from different subjects.

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