  The general ability to analyze and classify the 3D kinematics of the human
form 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 represent
and characterize actions such as RGB videos, infrared maps, and optical flow.
In particular, skeleton sequences provide a natural 3D kinematic description of
human motions and can be acquired in real time using RGB+D cameras. Moreover,
skeleton sequences are generalizable to characterize the motions of both humans
and humanoid robots. The Globally Optimal Reparameterization Algorithm (GORA)
is a novel, recently proposed algorithm for signal alignment in which signals
are reparameterized to a globally optimal universal standard timescale (UST).
Here, we introduce a variant of GORA for humanoid action recognition with
skeleton sequences, which we call GORA-S. We briefly review the algorithm's
mathematical foundations and contextualize them in the problem of action
recognition with skeleton sequences. Subsequently, we introduce GORA-S and
discuss parameters and numerical techniques for its effective implementation.
We then compare its performance with that of the DTW and FastDTW algorithms, in
terms of computational efficiency and accuracy in matching skeletons. Our
results show that GORA-S attains a complexity that is significantly less than
that of any tested DTW method. In addition, it displays a favorable balance
between speed and accuracy that remains invariant under changes in skeleton
sampling frequency, lending it a degree of versatility that could make it
well-suited for a variety of action recognition tasks.

Computer Vision and Pattern Recognition;Optimization and Control