Pierre Boulanger, Minh Tu Pham, Richard Moreau, Jenny Cifuentes, Flavio Prieto, Universidad de La Salle [Bogotá, Colombia], Ampère, Département Méthodes pour l'Ingénierie des Systèmes (MIS), Ampère (AMPERE), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Universidad Nacional de Colombia Sede Manizales, Universidad Nacional de Colombia, AMMI Lab, and University of Alberta
International audience; Minimally Invasive Surgery (MIS) has become widespread as an important surgical technique due to its advantages related to pain relief and short recovery time periods. However, this approach implies the acquisition of special surgical skills, which represents a challenge in the objective assessment of surgical gestures. In this way, several studies shown that kinematics and kinetic analysis of hand movement is a valuable assessment tool of basic surgical skills in MIS. In addition, recent researches proved that human motion performed during surgery can be described as a sequence of constant affine velocity movements. In this paper, we present a novel method to classify gestures based on an affine velocity analysis of 3D motion and an implementation of the Dynamic Time Warping algorithm. In particular, affine velocity calculation correlates kinematics and geometrical variables such as curvature, torsion, and euclidean velocity, reducing the dimension of the conventional 3D problem. In this way, using the simplicity of dynamic time warping algorithm allows us to perform an accurate classification, easier to implement and understand. Experimental validation of the algorithm is presented based on the position and orientation data of a laparoscope instrument, determiMinimally Invasive Surgery (MIS) has become widespread as an important surgical technique due to its advantages related to pain relief and short recovery time periods. However, this approach implies the acquisition of special surgical skills, which represents a challenge in the objective assessment of surgical gestures. In this way, several studies shown that kinematics and kinetic analysis of hand movement is a valuable assessment tool of basic surgical skills in MIS. In addition, recent researches proved that human motion performed during surgery can be described as a sequence of constant affine velocity movements. In this paper, we present a novel method to classify gestures based on an affine velocity analysis of 3D motion and an implementation of the Dynamic Time Warping algorithm. In particular, affine velocity calculation correlates kinematics and geometrical variables such as curvature, torsion, and euclidean velocity, reducing the dimension of the conventional 3D problem. In this way, using the simplicity of dynamic time warping algorithm allows us to perform an accurate classification, easier to implement and understand. Experimental validation of the algorithm is presented based on the position and orientation data of a laparoscope instrument, determined by six cameras. Results show the advantages of the proposed method compared to conventional Multidimensional Dynamic Time Warping to classify surgical gestures in MIS.ned by six cameras. Results show the advantages of the proposed method compared to conventional Multidimensional Dynamic Time Warping to classify surgical gestures in MIS.