@inproceedings{8ff7630bf73c4df2bb797de0d8b731e6,
title = "An Object State Estimation for the Peg Transfer Task in Computer-Guided Surgical Training",
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.",
keywords = "object recognition, object state detection, simulation-based surgical training",
author = "Kai Meisner and Minsik Hong and Rozenblit, {Jerzy W.}",
note = "Funding Information: This material is based upon work supported by the National Science Foundation under Grant Number; 2020 Spring Simulation Conference, SpringSim 2020 ; Conference date: 18-05-2020 Through 21-05-2020",
year = "2020",
month = may,
doi = "10.22360/SpringSim.2020.MSM.004",
language = "English (US)",
series = "Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Barros, {Fernando J.} and Xiaolin Hu and Hamdi Kavak and {Del Barrio}, {Alberto A.}",
booktitle = "Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020",
}