Measuring movement expertise in surgical tasks

Kanav Kahol, Narayanan C. Krishnan, Vineeth N. Balasubramanian, Sethuraman Panchanathan, Marshall Smith, John Ferrara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

Surgical movement is composed of discrete gestures that are combined to perform complex surgical procedures. A promising approach to objective surgical skill evaluation systems is kinematics and kinetic analysis of hand movement that yields a gesture level analysis of proficiency of a performed movement. In this paper, we propose a novel system that combines surgical gesture segmentation, surgical gesture recognition, and expertise analysis of surgical profiles in minimally invasive surgery (MIS). Kinematic analysis was used to segment gestures from a continuous motion stream. Human anatomy driven Hidden Markov Models (HMMs) are adopted for gesture recognition and expertise identification. When the proposed system was tested on a library of 200 samples for every basic surgical gesture, the gesture recognition module reported a perfect accuracy rate for the basic gestures, while the expertise identification module showed 94.7% accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the 14th Annual ACM International Conference on Multimedia, MM 2006
Pages719-722
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event14th Annual ACM International Conference on Multimedia, MM 2006 - Santa Barbara, CA, United States
Duration: Oct 23 2006Oct 27 2006

Publication series

NameProceedings of the 14th Annual ACM International Conference on Multimedia, MM 2006

Other

Other14th Annual ACM International Conference on Multimedia, MM 2006
Country/TerritoryUnited States
CitySanta Barbara, CA
Period10/23/0610/27/06

Keywords

  • Gesture recognition
  • Surgical motion
  • Surgical skill evaluation

ASJC Scopus subject areas

  • Computer Science(all)
  • Media Technology

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