Publication

Unsupervised trajectory segmentation and gesture recognition through curvature analysis and the Levenshtein distance

Conference Article

Conference

Iberian Robotics Conference (ROBOT)

Edition

7th

Pages

1-8

Doc link

https://doi.org/10.1109/ROBOT61475.2024.10796932

File

Download the digital copy of the doc pdf document

Abstract

This work discusses segmentation and gesture recognition in human-driven robotic trajectories, a technique with applications in several sectors such as in robot-assisted minimally invasive surgery (RMIS) training. By decomposing entire movements-gestures-into smaller actions -sub-gestures-, we can address gesture recognition accurately. This paper extends a bottom-up approach used in surgical gesture segmentation and incorporates natural language processing (NLP) techniques to match sub-gestures with letters and treating gestures as words. We evaluated our algorithm using two different datasets with trajectories on 2D and 3D. This NLP-inspired model obtains an average F1-score of 94.25% in the segmentation tasks, an accuracy of 87.05% in the learning stage, and an overall accuracy of 88.79% in the fully automated execution. These results indicate that the method effectively identifies and interprets new surgical gestures autonomously without the need for human intervention.

Categories

robot kinematics, robot programming, robots.

Scientific reference

G. Tapia, A. Colomé and C. Torras. Unsupervised trajectory segmentation and gesture recognition through curvature analysis and the Levenshtein distance, 7th Iberian Robotics Conference, 2024, Madrid, Spain, pp. 1-8.