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 language | English (US) |
---|---|
Title of host publication | Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020 |
Editors | Fernando J. Barros, Xiaolin Hu, Hamdi Kavak, Alberto A. Del Barrio |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781565553705 |
DOIs | |
State | Published - May 2020 |
Event | 2020 Spring Simulation Conference, SpringSim 2020 - Virtual, Fairfax, United States Duration: May 18 2020 → May 21 2020 |
Publication series
Name | Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020 |
---|
Conference
Conference | 2020 Spring Simulation Conference, SpringSim 2020 |
---|---|
Country | United States |
City | Virtual, Fairfax |
Period | 5/18/20 → 5/21/20 |
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