Modeling Realistic Tool-Tissue Interactions with Haptic Feedback: A Learning-based Method

Surgical simulators present a safe, practical, and ethical method for surgical training. In order to enhance realism and provide the user with an immersive training experience, simulators should have the capability to provide haptic feedback to the user. High-fidelity surgical simulators also requir...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems s. 209 - 215
Hlavní autoři: Pezzementi, Zachary, Ursu, Daniel, Misra, Sarthak, Okamura, Allison M.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.03.2008
Témata:
Biological tissues
Brain modeling
Computational modeling
Deformable models
Feedback
H.5.2 [Information Systems]: Information Interfaces and Presentation-User InterfacesHaptic I/O
Haptic interfaces
I.6.5 [Computing Methodologies]: Simulation and Modeling-Model Development
Learning systems
Surgery
Surgical instruments
Viscosity
ISBN:1424420059, 9781424420056
ISSN:2324-7347
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Surgical simulators present a safe, practical, and ethical method for surgical training. In order to enhance realism and provide the user with an immersive training experience, simulators should have the capability to provide haptic feedback to the user. High-fidelity surgical simulators also require accurate modeling of the interaction between surgical instruments and organs. Linear elasticity- based models are commonly used to simulate tool-tissue interaction due to computational considerations, although real soft tissues exhibit nonlinear viscoelastic behavior. In this paper, we use a learning algorithm to train a linear 2D mass-spring-damper system that behaves similarly to a high-fidelity nonlinear finite element (FE) model. The spring parameters are. trained off-line using data from an FE simulation of brain tissue deformation using simultaneous perturbation stochastic approximation, a model-free optimization algorithm. The model is implemented in a real-time soft tissue simulator with haptic interaction provided through the PHANTOM Omni haptic device. Our model's response is significantly closer to the desired response of the FE model than that of a linear heuristic model.
ISBN:1424420059
9781424420056
ISSN:2324-7347
DOI:10.1109/HAPTICS.2008.4479944