A Cost-Effective and Smart Sensing Tissue-like Testbed for Surgical Training

Abstract

A low-cost tissue-like testbed with six nodes of varying stiffness was developed for surgical training to provide pressure and force feedback data through image reception to human operators. Using SolidWorks, a 3D model of the box trainer housing was created. A pad for the distribution of smartsensing nodes and microcontroller connections was designed with open spaces for the respective components. The pad was 3D-printed with PLA filament. Flat piezoelectric pressure sensors were fabricated with conductive materials and velostat sensor material. Using static and dynamic analyses, three top sensors were chosen to be used in three pressure sensing nodes. A calibration process was performed on the pressure sensors to find the linear relationship between voltage and pressure, which was then used to create a conversion equation for each sensor. These equations were used to collect data at the three pressure sensing nodes on the silicone testbed pad. Conductive TPU filament was used to 3D-print vertical force sensors, which were designed using SolidWorks. The force sensors were calibrated with a squeezing mechanism to find a relationship between voltage and force and to subsequently develop a conversion equation for each sensor. We used these equations to collect force data from the three force sensing nodes on the testbed pad. Through static and dynamic analyses, the force sensors were found to be functional, but to need improvements in accuracy. The mechatronic system was designed and developed to integrate all six sensors and to collect data from the testbed pad using an Arduino microcontroller. The flat pressure and vertical force sensors were embedded in each node to measure the pressure and force that occurs during the deformation of the six nodes. Data was collected and imported into MATLAB. This data was used in displaying pressure and force mapping of the nodes over a live video of the silicone pad. Pressure and force mapping was realized by drawing color-coded circles on each of the six nodes that correspond to a range of force or pressure values. From this development, the surgical testbed provides multi-stiffness tissue training with live pressure and force mapping overlaid on a live video of the emulated surgical field.