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The general ability to analyze and classify the 3D kinematics of the humanform is an essential step in the development of socially adept humanoid robots.A variety of different types of signals can be used by machines to representand characterize actions such as RGB videos, infrared maps, and optical flow.In particular, skeleton sequences provide a natural 3D kinematic description ofhuman motions and can be acquired in real time using RGB+D cameras. Moreover,skeleton sequences are generalizable to characterize the motions of both humansand humanoid robots. The Globally Optimal Reparameterization Algorithm (GORA)is a novel, recently proposed algorithm for signal alignment in which signalsare reparameterized to a globally optimal universal standard timescale (UST).Here, we introduce a variant of GORA for humanoid action recognition withskeleton sequences, which we call GORA-S. We briefly review the algorithm'smathematical foundations and contextualize them in the problem of actionrecognition with skeleton sequences. Subsequently, we introduce GORA-S anddiscuss parameters and numerical techniques for its effective implementation.We then compare its performance with that of the DTW and FastDTW algorithms, interms of computational efficiency and accuracy in matching skeletons. Ourresults show that GORA-S attains a complexity that is significantly less thanthat of any tested DTW method. In addition, it displays a favorable balancebetween speed and accuracy that remains invariant under changes in skeletonsampling frequency, lending it a degree of versatility that could make itwell-suited for a variety of action recognition tasks.

Signal Alignment for Humanoid Skeletons via the Globally Optimal Reparameterization Algorithm