{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/190407302","title":"Automatic alignment of surgical videos using kinematic data","arxiv_id":"1904.07302","date":"2019-04-03","proceeding":null,"authors":["Hassan Ismail Fawaz","Germain Forestier","Jonathan Weber","François Petitjean","Lhassane Idoumghar","Pierre-Alain Muller"],"abstract":"Over the past one hundred years, the classic teaching methodology of \"see\none, do one, teach one\" has governed the surgical education systems worldwide.\nWith the advent of Operation Room 2.0, recording video, kinematic and many\nother types of data during the surgery became an easy task, thus allowing\nartificial intelligence systems to be deployed and used in surgical and medical\npractice. Recently, surgical videos has been shown to provide a structure for\npeer coaching enabling novice trainees to learn from experienced surgeons by\nreplaying those videos. However, the high inter-operator variability in\nsurgical gesture duration and execution renders learning from comparing novice\nto expert surgical videos a very difficult task. In this paper, we propose a\nnovel technique to align multiple videos based on the alignment of their\ncorresponding kinematic multivariate time series data. By leveraging the\nDynamic Time Warping measure, our algorithm synchronizes a set of videos in\norder to show the same gesture being performed at different speed. We believe\nthat the proposed approach is a valuable addition to the existing learning\ntools for surgery.","url_abs":"http://arxiv.org/abs/1904.07302v2","url_pdf":"http://arxiv.org/pdf/1904.07302v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"190407302","repo_url":"https://github.com/hfawaz/aime19","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.07302","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}