Van Amsterdam, B;
Nakawala, H;
De Momi, E;
Stoyanov, D;
(2019)
Weakly Supervised Recognition of Surgical Gestures.
In:
Proceedings of the 2019 International Conference on Robotics and Automation (ICRA).
(pp. pp. 9565-9571).
IEEE: Montreal, Canada.
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Abstract
Kinematic trajectories recorded from surgical robots contain information about surgical gestures and potentially encode cues about surgeon’s skill levels. Automatic segmentation of these trajectories into meaningful action units could help to develop new metrics for surgical skill assessment as well as to simplify surgical automation. State-of-the-art methods for action recognition relied on manual labelling of large datasets, which is time consuming and error prone. Unsupervised methods have been developed to overcome these limitations. However, they often rely on tedious parameter tuning and perform less well than supervised approaches, especially on data with high variability such as surgical trajectories. Hence, the potential of weak supervision could be to improve unsupervised learning while avoiding manual annotation of large datasets. In this paper, we used at a minimum one expert demonstration and its ground truth annotations to generate an appropriate initialization for a GMM-based algorithm for gesture recognition. We showed on real surgical demonstrations that the latter significantly outperforms standard task-agnostic initialization methods. We also demonstrated how to improve the recognition accuracy further by redefining the actions and optimising the inputs
| Type: | Proceedings paper |
|---|---|
| Title: | Weakly Supervised Recognition of Surgical Gestures |
| Event: | International Conference on Robotics and Automation 2019 |
| Location: | Montreal |
| Dates: | 20 May 2019 - 24 May 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICRA.2019.8793696 |
| Publisher version: | https://doi.org/10.1109/ICRA.2019.8793696 |
| 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. |
| Keywords: | Classification, Gaussian Mixture Models, robotic surgery, kinematics, surgical gesture recognition |
| 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10077874 |
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