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Joints-space metrics for automatic robotic surgical gestures classification

IEEE International Conference on Intelligent Robots and Systems, ISSN: 2153-0866, Page: 3061-3066
2020
  • 3
    Citations
  • 0
    Usage
  • 11
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    3
    • Citation Indexes
      3
  • Captures
    11

Conference Paper Description

Automated surgical gestures classification and recognition are important precursors for achieving the goal of objective evaluation of surgical skills. Many works have been done to discover and validate metrics based on the motion of instruments that can be used as features for automatic classification of surgical gestures. In this work, we present a series of angular metrics that can be used together with Cartesian-based metrics to better describe different surgical gestures. These metrics can be calculated both in Cartesian and joint space, and they are used in this work as features for automatic classification of surgical gestures. To evaluate the proposed metrics, we introduce a novel surgical dataset that contains both Cartesian and joint spaces data acquired with da Vinci Research Kit (dVRK) while a single expert operator is performing 40 subsequent suturing exercises. The obtained results confirm that the application of metrics in the joint space improves the accuracy of automatic gesture classification.

Bibliographic Details

Marco Bombieri; Diego Dall'Alba; Sanat Ramesh; Giovanni Menegozzo; Paolo Fiorini; Caitlin Schneider

Institute of Electrical and Electronics Engineers (IEEE)

Engineering; Computer Science

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