@inproceedings{0ab56cc3e3664bbeb075f4993f1abb9c,
title = "Automatic detection and segmentation of robot-assisted surgical motions",
abstract = "Robotic surgical systems such as Intuitive Surgical's da Vinci system provide a rich source of motion and video data from surgical procedures. In principle, this data can be used to evaluate surgical skill, provide surgical training feedback, or document essential aspects of a procedure. If processed online, the data can be used to provide context-specific information or motion enhancements to the surgeon. However, in every case, the key step is to relate recorded motion data to a model of the procedure being performed. This paper examines our progress at developing techniques for {"}parsing{"} raw motion data from a surgical task into a labelled sequence of surgical gestures. Our current techniques have achieved >90% fully automated recognition rates on 15 datasets.",
author = "Lin, {Henry C.} and Izhak Shafran and Murphy, {Todd E.} and Okamura, {Allison M.} and Yuh, {David D.} and Hager, {Gregory D.}",
note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 8th International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 ; Conference date: 26-10-2005 Through 29-10-2005",
year = "2005",
doi = "10.1007/11566465_99",
language = "English (US)",
isbn = "3540293272",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "802--810",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2005 - 8th International Conference, Proceedings",
}