03 de julio de 2015
Resumen:
One of the most difficult tasks in surgical education is to teach students what is the optimal magnitude of forces and torques to guide the instrument during operation. This problem becomes even more relevant in the field of Mini Invasive Surgery (MIS), where the depth perception is lost and visual field is reduced. In this way, the evaluation of surgical skills involved in this field becomes in a critical point in the learning process. Nowadays, this assessment is performed by expert surgeons observation in di↵erent operating rooms, making evident subjectivity issues in the results depending on the trainer in charge of the task.
Research works around the world have focused on the development of the automated evaluation techniques, that provide an objective feedback during the learning process. Therefore, first part of this thesis describe a new method of classification of 3D medical gestures based on biomechanical models (kinematics). This new approach analyses medical gestures based on the smoothness and quality of movements related to the tasks performed during the medical training. Thus, gesture classification is accomplished using an arc length parametrization to compute the curvature for each trajectory. The advantages of this approach are mainly oriented towards time and location independence and problem simplification. The study included several gestures that were performed repeatedly by di↵erent subjects; these data sets were acquired, also, with three di↵erent devices.
Second part of this work is focused in a classification technique based on kinematic and dynamic data. In first place, an empirical expression between movement geometry and kinematic data is used to compute a di↵erent variable called the affine velocity. Experiments carried out in this work show the constant nature of this feature in basic medical gestures. In the same way, results proved an adequate classification based on this computation. Parameters found in previous experiments were taken into account to study movements more complex.
Likewise, affine velocity was used to perform a segmentation of pick and release tasks, and the classification stage was completed using an energy computation, based on dynamic data, for each segment. Final experiments were performed using six video cameras and an instrumented laparoscope. The 3-D position of the end e↵ector was recorded, for each participant, using the OptiTrack Motive Software and reflective markers mounted on the laparoscope.
Force and torque measurements, on the other hand, were acquired using force and torque sensors attached to the instrument and located between the tool tip and the handle of the tool in order to capture the interaction between participant and the manipulated material.
Results associated to these experiments present a correlation between the energy values and the surgical skills of the participants involved in these experiments.
Palabras clave: Affine Velocity; Curvature Analysis; Dynamic Arc Length Warping; Energy; Gesture classification; Hand Motion Tracking; One-Sixth Power Law; Segmentation.
Cita:
J. Cifuentes (2015), Development of a new technique for objective assessment of gestures in mini-invasive surgery. Bogotá (Colombia).
