Vision-Based Force Estimation for Minimally Invasive Telesurgery Through Contact Detection and Local Stiffness Models

Concetti Chiave

Vision-based contact-conditional force estimation shows promise for sensory substitution haptic feedback in telesurgery.

Sintesi

Accurate force sensing in minimally invasive telesurgery is challenging.
Vision-based force estimation using deep learning offers a promising alternative.
A novel contact-conditional approach for force estimation is presented.
The method combines supervised learning with human labels and end effector position data.
Results show high accuracy in contact detection and force prediction.
The approach demonstrates potential for sensory substitution haptic feedback in clinical settings.
Data efficiency and generalization to novel surgical scenarios are explored.

Statistiche

Predictions from trained models show greater than 90% accuracy on contact detection and less than 10% force prediction error.
The average error in stiffness estimation between different models ranges from 13% to 19%.
Contact-conditional force estimation methods outperform traditional force estimation approaches.

Citazioni

"Vision-based force sensing approaches using deep learning are a promising alternative to intrinsic end effector force sensing."
"Our methods demonstrated greater than 90% accuracy on contact detection and less than 10% force prediction error."

Approfondimenti chiave tratti da

Vision-Based Force Estimation for Minimally Invasive Telesurgery Through Contact Detection and Local Stiffness Models

by Shuyuan Yang... alle arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18172.pdf

Domande più approfondite

How can trocar-based force sensing be integrated into the force estimation approach

Integrating trocar-based force sensing into the force estimation approach can enhance the accuracy and reliability of the system. Trocars are commonly used in minimally invasive surgery to create a pathway for surgical instruments. By incorporating force sensors into trocars, the system can capture the forces exerted at the entry point into the body. This data can then be used to complement the force estimation derived from the end effector and robot joint torque information.
The integration of trocar-based force sensing would provide additional data points for the system to analyze, offering a more comprehensive understanding of the forces at play during surgery. By combining the information from the trocar sensors with the existing force estimation methods, the system can create a more robust model for force estimation in telesurgery. This integration can lead to more accurate and precise force feedback, enhancing the overall safety and effectiveness of the surgical procedure.

What are the implications of slip detection on the accuracy of force estimation in telesurgery

Slip detection plays a crucial role in the accuracy of force estimation in telesurgery. In surgical procedures, slip can occur when the surgical instrument loses contact with the tissue or experiences a change in the frictional forces between the instrument and the tissue. Detecting slip is essential as it can impact the accuracy of force feedback and the effectiveness of tissue manipulation.
In the context of force estimation, slip detection can help differentiate between intentional movements and unintended slips. By incorporating slip detection mechanisms into the system, the force estimation algorithm can adjust the calculations based on the detected slip events. This adjustment can prevent inaccurate force readings caused by slips and ensure that the force feedback provided to the surgeon is reliable and reflective of the actual tissue interaction.
Overall, slip detection enhances the precision and reliability of force estimation in telesurgery by accounting for potential discrepancies in the forces applied during the procedure. It contributes to the overall safety and efficacy of the surgical process by ensuring that the force feedback provided to the surgeon is accurate and responsive to the actual tissue handling dynamics.

How can non-linear stiffness models improve the quality of force estimation in tissue handling skill evaluation

Non-linear stiffness models offer a more sophisticated approach to force estimation in tissue handling skill evaluation. Traditional linear stiffness models may not capture the complex behavior of tissues under varying forces and deformations. Non-linear models can better represent the non-linear relationship between force and displacement in tissues, providing a more accurate estimation of the forces involved in tissue manipulation.
By incorporating non-linear stiffness models into the force estimation approach, the system can better account for the tissue's response to different forces, including behaviors such as strain hardening or softening. This enhanced modeling can improve the accuracy of force estimation, especially in scenarios where tissues undergo significant deformations or nonlinear responses to forces.
Additionally, non-linear stiffness models can offer more flexibility in capturing the intricacies of tissue behavior, allowing for a more nuanced evaluation of tissue handling skills. By incorporating these models, the system can provide more detailed and accurate feedback on the forces exerted during surgery, leading to improved skill assessment and training outcomes in telesurgery.