title: Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training.
abstract: Dexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us 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 that 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 toward improving training efficiency by minimizing the learning curve.
authors.forename: Fabien
authors.initials: F
authors.lastname: Despinoy
authors.forename: David
authors.initials: D
authors.lastname: Bouget
authors.forename: Germain
authors.initials: G
authors.lastname: Forestier
authors.forename: Cedric
authors.initials: C
authors.lastname: Penet
authors.forename: Nabil
authors.initials: N
authors.lastname: Zemiti
authors.forename: Philippe
authors.initials: P
authors.lastname: Poignet
authors.forename: Pierre
authors.initials: P
authors.lastname: Jannin
mesh_terms: D005868:Gestures; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D065287:Robotic Surgical Procedures / Q000193:education* / Q000379:methods*; D000069558:Unsupervised Machine Learning