Session: 06-05-02: Biomedical Devices, Sensors, and Actuators

Paper Number: 145895

145895 - A Cost-Effective and Tissue-Sensing Simulator for Robotic Minimally Invasive Surgery Training 

Introduction

Over the last three decades, robotic minimally invasive surgery (RMIS) has been universally accepted for many surgical applications, significantly reducing morbidity, readmission, and reoperation procedures. Compared to open surgery, RMIS requires unique technical skills that form steep learning curves that doctors must train through.
Current box trainers or simulators only allow for the practice of generic skills without force feedback, while the more adequate virtual reality simulation methods are expensive, making effective RMIS training relatively inaccessible. In this work, we explore the integration of tissue-responsive force feedback and visual feedback in a surgical simulator, aiming to adapt to impaired depth perception, reduced tactile feedback in soft tissue, and amplified tremors via the fulcrum effect for the RMIS training. The simulator will be the affordable device practiced on by the surgeon in training with two unique capabilities: 1) as a training simulator that can be used on its own for more accessible and effective RMIS training practices with pressure and image feedback, and 2) as a simulation module that can be integrated into more extensive and general telesurgical robotic training systems.

Contribution of the work

Our contribution lies in devising a cost-effective solution by leveraging 3D printing, laser cutting, and available lab materials. We aim to implement pressure sensors directly beneath the tissue, enhancing trainee interaction. Furthermore, we propose a novel visual feedback system using color-based pressure mapping to illustrate pressure distribution across the tissue pad. This innovative approach offers a comprehensive understanding of pressure application, improving the effectiveness of laparoscopic training while optimizing resource utilization.

Methodology

We design and develop a low-cost tissue-like surgical simulator with six nodes of varying stiffness for telesurgical training, providing force feedback and visual feedback to human operators. Using SolidWorks, a 3D model of the smart sensing simulator is designed with open spaces for sensors and wires for microcontroller connections. A silicone tissue pad mold is then designed, and the mold pieces are 3D printed with PETG filament. Piezoelectric sensors are fabricated with conductive materials and velostats pressure sensors. Sensors are housed in silicone nodes with tissue-like stiffness to collect surface pressure and apply force on the simulator. Conductive filament is used to print 3D strain gauges. The strain gauges are embedded in each node to measure the stretch and deformation of the nodes. A mechatronic system is designed to integrate all sensors to collect data from the simulator. Data is processed in MATLAB after signal processing and noise filtering. We plan to test the sustainability and the repeatability of the smart sensing simulator with human participants. Human-centric data is classified using Fitts’ Law. The overall performance of human operation is evaluated after each experiment. The cost-effective and smart sensing tissue-like surgical simulator is a powerful tool to use in telesurgical clinical training settings, with the ability to provide the best and most intuitive feedback for professional trainers.

Preliminary results and conclusions

Current solutions for surgical training, such as the traditional box trainer, do not provide adequate training in terms of the skills practice and performance feedback they offer. We developed a tissue sampling pad consisting of a rectangular silicone pad with six nodes, each featuring different silicone stiffness to emulate the range of stiffness found in the body from fat tissue to tumors. In this tissue pad design, pressure feedback is obtained through a piezoelectric sensor placed below each node. Static and dynamic tests were carried out to determine the effectiveness of the in-house fabricated sensors for measuring pressure. The sensors showed significant sensitivity and fast enough response to be sufficient for the pressure measurement purpose. The completion of the overall RMIS simulator will be followed with human-centric experiments based on Fitts’ Law to evaluate the efficacy of trainees in performing training tasks. Bi-directional teleoperation will be utilized for both free and haptic modes of operation. 

Presenting Author: Jenny Huynh San Jose State University

Presenting Author Biography: Jenny Huynh is a Graduate Research Assistant at San Jose State University working on new and innovative technologies in the biomedical and surgical robotics industry. Her research helps bridge the gap between telerobotic systems and their impact in minimally invasive surgery. She currently works as a Spacecraft Engineer at Planet developing new technologies to provide useful data on combating global climate change. She hopes to continue her research to expand the applications of telerobotic operation for development in the space industry.

Authors:

Lysette Zaragoza San Jose State University
Jenny Huynh San Jose State University
Joshua Billmann San Jose State University
Eric Barlog San Jose State University
Gaojian Huang San Jose State University
Egbe-Etu Etu San Jose State University
Lin Jiang San Jose State University