@inproceedings{368c7aca04754cfeb9a82bec675f31ef,
title = "Automatic recognition of surgical motions using statistical modeling for capturing variability",
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.",
keywords = "Robotic surgery, Surgical skill evaluation, Suturing",
author = "Reiley, {Carol E.} and Lin, {Henry C.} and Balakrishnan Varadarajan and Balazs Vagvolgyi and Sanjeev Khudanpur and Yuh, {David D.} and Hager, {Gregory D.}",
year = "2008",
month = jan,
day = "1",
language = "English (US)",
isbn = "9781586038229",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "396--401",
booktitle = "Medicine Meets Virtual Reality 16 - Parallel, Combinatorial, Convergent",
note = "Medicine Meets Virtual Reality 16 - Parallel, Combinatorial, Convergent: NextMed by Design, MMVR 2008 ; Conference date: 30-01-2008 Through 01-02-2008",
}