String Motif-Based Description of Tool Motion for Detecting Skill and Gestures in Robotic Surgery

  • Narges Ahmidi
  • Yixin Gao
  • Benjamín Béjar
  • S. Swaroop Vedula
  • Sanjeev Khudanpur
  • René Vidal
  • Gregory D. Hager
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)

Abstract

The growing availability of data from robotic and laparoscopic surgery has created new opportunities to investigate the modeling and assessment of surgical technical performance and skill. However, previously published methods for modeling and assessment have not proven to scale well to large and diverse data sets. In this paper, we describe a new approach for simultaneous detection of gestures and skill that can be generalized to different surgical tasks. It consists of two parts: (1) descriptive curve coding (DCC), which transforms the surgical tool motion trajectory into a coded string using accumulated Frenet frames, and (2) common string model (CSM), a classification model using a similarity metric computed from longest common string motifs. We apply DCC-CSM method to detect surgical gestures and skill levels in two kinematic datasets (collected from the da Vinci surgical robot). DCC-CSM method classifies gestures and skill with 87.81% and 91.12% accuracy, respectively.

Keywords

surgical motion descriptive models gesture and skill classification geometry descriptive curve coding robotic surgery 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Narges Ahmidi
    • 1
  • Yixin Gao
    • 1
  • Benjamín Béjar
    • 2
  • S. Swaroop Vedula
    • 1
  • Sanjeev Khudanpur
    • 1
    • 3
  • René Vidal
    • 1
    • 2
    • 3
  • Gregory D. Hager
    • 1
  1. 1.Department of Computer ScienceJohns Hopkins UniversityBaltimoreUSA
  2. 2.Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA
  3. 3.Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA

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