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1 LTSI - Laboratoire Traitement du Signal et de l-Image 2 DEXTER - Conception et commande de robots pour la manipulation LIRMM - Laboratoire d-Informatique de Robotique et de Microélectronique de Montpellier 3 MIPS - Modélisation, Intelligence, Processus et Système

Abstract : Dexterity and procedural knowledge are two critical skills surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow to provide an in-depth analysis of surgical gestures to precisely assess these skills. Our objective is to develop a method for the automatic and quantitative assessment of surgical gestures. To reach this goal, we propose a new unsupervised algorithm that can automatically segment kinematic data from robotic training sessions. Without relying on any prior information or model, this algorithm detects critical points in the kinematic data which define relevant spatio-temporal segments. Based on the association of these segments, we obtain an accurate recognition of the gestures involved in the surgical training task. We then perform an advanced analysis and assess our algorithm using datasets recorded during real expert training sessions. After comparing our approach with the manual annotations of the surgical gestures, we observe 97.4% accuracy for the learning purpose and an average matching score of 81.9% for the fully-automated gesture recognition process. Our results show that trainees workflow can be followed and surgical gestures may be automatically evaluated according to an expert database. This approach tends towards improving training efficiency by minimizing the learning curve.

Keywords : Classification Teleoperation Surgical Skills Training Robotic Surgery Machine Learning Surgical Gesture Recognition Unsupervised Trajectory Segmentation

Autor: Fabien Despinoy - David Bouget - Germain Forestier - Cédric Penet - Nabil Zemiti - Philippe Poignet - Pierre Jannin -

Fuente: https://hal.archives-ouvertes.fr/


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