Name
Abstract | ||
|---|---|---|
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, 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8037309 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Affine transformation,Computer vision,Curvature,Kinematics,Torsion (mechanics),Dynamic time warping,Computer science,Gesture,Artificial intelligence,Euclidean geometry,Gesture classification | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jenny Cifuentes | 1 | 2 | 1.87 |
| Minh Tu Pham | 2 | 76 | 15.53 |
| Richard Moreau | 3 | 6 | 1.30 |
| Flavio Prieto | 4 | 24 | 9.63 |
| Boulanger, P. | 5 | 28 | 10.62 |