An object state estimation for the peg transfer task in computer-guided surgical training

Kai Meisner, Minsik Hong, Jerzy W. Rozenblit

Research output: Contribution to journalConference article

Abstract

Computer-based simulators have been developed to enhance training experiences in laparoscopic surgical skills training. Most simulators can evaluate a trainee's performance objectively. However, only few simulators can provide active guidance features such as audio and visual guidance. In this paper, an object state estimation and tracking method is presented to support visual and force guidance for computer-assisted surgical trainer (CAST) using image processing schemes in real-time fashion given a specific object transfer task. The experimental results show that the proposed tracking method reaches 100 frame per seconds and estimates an object state effectively for the standard laparoscopy peg transfer task.

Original languageEnglish (US)
Pages (from-to)627-638
Number of pages12
JournalSimulation Series
Volume52
Issue number1
StatePublished - 2020
Event2020 Spring Simulation Multiconference, SpringSim 2020 - Virtual, Online
Duration: May 18 2020May 21 2020

Keywords

  • Object recognition
  • Object state detection
  • Simulation-based surgical training

ASJC Scopus subject areas

  • Computer Networks and Communications

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