Mazomenos, E;
Watson, D;
Kotorov, R;
Stoyanov, D;
(2018)
Gesture Classification in Robotic Surgery using Recurrent Neural Networks with Kinematic Information.
In: Althoefer, Kaspar and Vander Poorten, Emmanuel, (eds.)
Proceedings of the 8th Joint Workshop on New Technology for Computer/Robot Assisted Surgery (CRAS 2018).
CRAS: London, UK.
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Abstract
In this work we introduce the application of Recurrent Neural Networks (RNNs) on surgical kinematic data, for the classification of gestures in three fundamental surgical tasks (suturing, needle passing knot tying). The developed RNN-based classifier achieves close to 60% average classification accuracy for all three tasks when trained and tested with dVSS kinematic data from the same operator. Our preliminary work indicates that this type of artificial neural networks can be the building blocks in gesture classification systems which can form the basis for further developing automated skill assessment methods in robotic surgery.
| Type: | Proceedings paper |
|---|---|
| Title: | Gesture Classification in Robotic Surgery using Recurrent Neural Networks with Kinematic Information |
| Event: | 8th Joint Workshop on New Technologies for Computer/Robotic Assisted Surgery (CRAS 2018, 10-11 September 2018, Queen Mary University of London |
| Location: | London, U.K. |
| Dates: | 10 September 2018 - 11 September 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.cras-eu.org/past%20events/cras-2018-pa... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10056615 |
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