Automatic alignment of surgical videos using kinematic data

Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, François Petitjean, Lhassane Idoumghar, Pierre Alain Muller

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine
Subtitle of host publication17th Conference on Artificial Intelligence in Medicine, AIME 2019 Poznan, Poland, June 26–29, 2019 Proceedings
EditorsDavid Riaño, Szymon Wilk, Annette ten Teije
Place of PublicationCham Switzerland
Number of pages10
ISBN (Electronic)9783030216429
ISBN (Print)9783030216412
Publication statusPublished - 2019
EventArtificial Intelligence in Medicine in Europe 2019 - Poznan, Poland
Duration: 26 Jun 201929 Jun 2019
Conference number: 17th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceArtificial Intelligence in Medicine in Europe 2019
Abbreviated titleAIME 2019
Internet address


  • Dynamic Time Warping
  • Multivariate time series
  • Surgical education
  • Video synchronization

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