A Pipeline for the Automatic Evaluation of Dental Surgery Gestures in Preclinical Training from Captured Motions
Résumé
This work in progress proposes an automatic evaluation pipeline for dental surgery gestures based on teacher’s
demonstrations and observation needs. This pipeline aims at supporting learning in preclinical situations for
the first years of study in the dental school. It uses the Random Forest (RF) algorithm to train a model based
on specific descriptors for each gesture component, that are designed to cover the evolution of the observation
needs. The inputs are the captured motion parts whose labels are defined by the teachers with their own
vocabulary, to represent expected or no-wanted geometrical or kinematic features. The overall evaluation (for
example, weighted average of each component) and the component evaluation can be given to students to
improve their postures and motor skills. A preliminary test correctly classifies a back correct posture and
three main flaws (”Twisted Back and Bent Head”, ”Leaning Back”, ”Leaning Back and Bent Back”) by the
RF model, for the posture component. This approach is designed for the adaptation to the expert’s evolving
observation needs while minimizing the need for a heavy re-engineering process and enhancing the system
acceptance
Domaines
Informatique [cs]
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[Hefied et al]_A Pipeline for the Automatic Evaluation of Dental Surgery Gestures in Preclinical Training from Captured Motions.pdf (4.35 Mo)
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