  Signal alignment has become a popular problem in robotics due in part to its
fundamental role in action recognition. Currently, the most successful
algorithms for signal alignment are Dynamic Time Warping (DTW) and its variant
'Fast' Dynamic Time Warping (FastDTW). Here we introduce a new framework for
signal alignment, namely the Globally Optimal Reparameterization Algorithm
(GORA). We review the algorithm's mathematical foundation and provide a
numerical verification of its theoretical basis. We compare the performance of
GORA with that of the DTW and FastDTW algorithms, in terms of computational
efficiency and accuracy in matching signals. Our results show a significant
improvement in both speed and accuracy over the DTW and FastDTW algorithms and
suggest that GORA has the potential to provide a highly effective framework for
signal alignment and action recognition.
