Automatic recognition of surgical motions using statistical modeling for capturing variability

Carol E. Reiley, Henry C. Lin, Balakrishnan Varadarajan, Balazs Vagvolgyi, Sanjeev Khudanpur, David D. Yuh, Gregory Hager

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

Abstract

The ability to accurately recognize elementary surgical gestures is a stepping stone to automated surgical assessment and surgical training. However, as the pool of subjects increases, variation in surgical techniques and unanticipated motion increases the challenge of creating robust statistical models of gestures. This paper examines the applicability of advanced modeling techniques from automated speech recognition to the problem of increasing variability in surgical motions. In particular, we demonstrate the effectiveness of automatically bootstrapped useradaptive models on diverse data acquired from the da Vinci surgical robot.

Original languageEnglish (US)
Title of host publicationStudies in Health Technology and Informatics
Pages396-401
Number of pages6
Volume132
StatePublished - 2008
EventMedicine Meets Virtual Reality 16 - Parallel, Combinatorial, Convergent: NextMed by Design, MMVR 2008 - Long Beach, CA, United States
Duration: Jan 30 2008Feb 1 2008

Other

OtherMedicine Meets Virtual Reality 16 - Parallel, Combinatorial, Convergent: NextMed by Design, MMVR 2008
CountryUnited States
CityLong Beach, CA
Period1/30/082/1/08

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Keywords

  • Robotic surgery
  • Surgical skill evaluation
  • Suturing

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Reiley, C. E., Lin, H. C., Varadarajan, B., Vagvolgyi, B., Khudanpur, S., Yuh, D. D., & Hager, G. (2008). Automatic recognition of surgical motions using statistical modeling for capturing variability. In Studies in Health Technology and Informatics (Vol. 132, pp. 396-401)