  Over the past one hundred years, the classic teaching methodology of "see
one, do one, teach one" has governed the surgical education systems worldwide.
With the advent of Operation Room 2.0, recording video, kinematic and many
other types of data during the surgery became an easy task, thus allowing
artificial intelligence systems to be deployed and used in surgical and medical
practice. Recently, surgical videos has been shown to provide a structure for
peer coaching enabling novice trainees to learn from experienced surgeons by
replaying those videos. However, the high inter-operator variability in
surgical gesture duration and execution renders learning from comparing novice
to expert surgical videos a very difficult task. In this paper, we propose a
novel technique to align multiple videos based on the alignment of their
corresponding kinematic multivariate time series data. By leveraging the
Dynamic Time Warping measure, our algorithm synchronizes a set of videos in
order to show the same gesture being performed at different speed. We believe
that the proposed approach is a valuable addition to the existing learning
tools for surgery.
