An Object State Estimation for the Peg Transfer Task in Computer-Guided Surgical Training

Kai Meisner, Minsik Hong, Jerzy W. Rozenblit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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)
Title of host publicationProceedings of the 2020 Spring Simulation Conference, SpringSim 2020
EditorsFernando J. Barros, Xiaolin Hu, Hamdi Kavak, Alberto A. Del Barrio
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781565553705
DOIs
StatePublished - May 2020
Event2020 Spring Simulation Conference, SpringSim 2020 - Virtual, Fairfax, United States
Duration: May 18 2020May 21 2020

Publication series

NameProceedings of the 2020 Spring Simulation Conference, SpringSim 2020

Conference

Conference2020 Spring Simulation Conference, SpringSim 2020
Country/TerritoryUnited States
CityVirtual, Fairfax
Period5/18/205/21/20

Keywords

  • object recognition
  • object state detection
  • simulation-based surgical training

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Modeling and Simulation

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