Video-based motion expertise analysis in simulation-based surgical training using Hierarchical Dirichlet Process Hidden Markov Model

Qiang Zhang, Baoxin Li

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

10 Scopus citations

Abstract

In simulation-based surgical training, a key task is to rate the performance of the operator, which is done currently by senior surgeons. This is a costly practice and objectively quantifiable assessment metrics are often missing. Researchers have been working towards building automated systems to achieve computational understanding of surgical skills, largely through analysis of motion data captured by video or other sensors. In this paper, we extend the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) for this purpose. We start with detecting spatial temporal interest points from the video capturing the tool motion of an operator, and then generate visual words from the descriptors of those interest points. For each frame, we construct a histogram with the associated interest points, i.e. the "bag of words", and then every video is represented by a sequence of those histograms. For sequences of each motion expertise level, we infer an HDP-HMM model. Finally, the classification of the motion expertise level for a testing sequence is based on choosing a model that maximizes the likelihood of the given sequence. Compared with the other action recognition algorithms, such as kernel SVM, our method leads to a better result. Further, the proposed approach also provides some important cues on the patterns of motion for each expertise level.

Original languageEnglish (US)
Title of host publicationMM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops - 2011 ACM International Workshop on Medical Multimedia Analysis and Retrieval, MMAR'11
Pages19-24
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event2011 ACM Multimedia Conference, MM'11 and Co-Located Workshops - 2011 ACM International Workshop on Medical Multimedia Analysis and Retrieval, MMAR'11 - Scottsdale, AZ, United States
Duration: Nov 28 2011Dec 1 2011

Publication series

NameMM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops - 2011 ACM International Workshop on Medical Multimedia Analysis and Retrieval, MMAR'11

Conference

Conference2011 ACM Multimedia Conference, MM'11 and Co-Located Workshops - 2011 ACM International Workshop on Medical Multimedia Analysis and Retrieval, MMAR'11
CountryUnited States
CityScottsdale, AZ
Period11/28/1112/1/11

    Fingerprint

Keywords

  • Dirichlet
  • HDP-HMM
  • Motion expertise
  • Surgery simulation
  • Video analysis

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Biomedical Engineering

Cite this

Zhang, Q., & Li, B. (2011). Video-based motion expertise analysis in simulation-based surgical training using Hierarchical Dirichlet Process Hidden Markov Model. In MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops - 2011 ACM International Workshop on Medical Multimedia Analysis and Retrieval, MMAR'11 (pp. 19-24). (MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops - 2011 ACM International Workshop on Medical Multimedia Analysis and Retrieval, MMAR'11). https://doi.org/10.1145/2072545.2072550