Sparse hidden Markov models for surgical gesture classification and skill evaluation

Lingling Tao, Ehsan Elhamifar, Sanjeev Khudanpur, Gregory D. Hager, René Vidal

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

Abstract

We consider the problem of classifying surgical gestures and skill level in robotic surgical tasks. Prior work in this area models gestures as states of a hidden Markov model (HMM) whose observations are discrete, Gaussian or factor analyzed. While successful, these approaches are limited in expressive power due to the use of discrete or Gaussian observations. In this paper, we propose a new model called sparse HMMs whose observations are sparse linear combinations of elements from a dictionary of basic surgical motions. Given motion data from many surgeons with different skill levels, we propose an algorithm for learning a dictionary for each gesture together with an HMM grammar describing the transitions among different gestures. We then use these dictionaries and the grammar to represent and classify new motion data. Experiments on a database of surgical motions acquired with the da Vinci system show that our method performs on par with or better than state-of-the-art methods.This suggests that learning a grammar based on sparse motion dictionaries is important in gesture and skill classification.

Original languageEnglish (US)
Title of host publicationInformation Processing in Computer-Assisted Interventions - Third International Conference, IPCAI 2012, Proceedings
Pages167-177
Number of pages11
DOIs
StatePublished - Jul 31 2012
Event3rd International Conference on Information Processing in Computer-Assisted Interventions, IPCAI 2012 - Pisa, Italy
Duration: Jun 27 2012Jun 27 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7330 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Information Processing in Computer-Assisted Interventions, IPCAI 2012
CountryItaly
CityPisa
Period6/27/126/27/12

Keywords

  • Surgical skill evaluation
  • hidden Markov models
  • sparse dictionary learning
  • surgical gesture classification
  • time series classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tao, L., Elhamifar, E., Khudanpur, S., Hager, G. D., & Vidal, R. (2012). Sparse hidden Markov models for surgical gesture classification and skill evaluation. In Information Processing in Computer-Assisted Interventions - Third International Conference, IPCAI 2012, Proceedings (pp. 167-177). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7330 LNCS). https://doi.org/10.1007/978-3-642-30618-1_17