HySenSe: A Hyper-Sensitive and High-Fidelity Vision-Based Tactile Sensor

In this paper, to address the sensitivity and durability trade-off of Vision-based Tactile Sensor (VTSs), we introduce a hyper-sensitive and high-fidelity VTS called HySenSe. We demonstrate that by solely changing one step during the fabrication of the gel layer of the GelSight sensor (as the most well-known VTS), we can substantially improve its sensitivity and durability. Our experimental results clearly demonstrate the outperformance of the HySenSe compared with a similar GelSight sensor in detecting textural details of various objects under identical experimental conditions and low interaction forces (<= 1.5 N).Comment: Accepted to IEEE Sensors 2022 Conferenc

Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing

In this study, to address the current high earlydetection miss rate of colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer learning and machine learning (ML) classifiers to precisely and sensitively classify the type of CRC polyps. Instead of using the common colonoscopic images, we applied three different ML algorithms on the 3D textural image outputs of a unique vision-based surface tactile sensor (VS-TS). To collect realistic textural images of CRC polyps for training the utilized ML classifiers and evaluating their performance, we first designed and additively manufactured 48 types of realistic polyp phantoms with different hardness, type, and textures. Next, the performance of the used three ML algorithms in classifying the type of fabricated polyps was quantitatively evaluated using various statistical metrics.Comment: Accepted to IEEE Sensors 2022 Conferenc

Towards Biomechanics-Aware Design of a Steerable Drilling Robot for Spinal Fixation Procedures with Flexible Pedicle Screws

Towards reducing the failure rate of spinal fixation surgical procedures in osteoporotic patients, we propose a unique biomechanically-aware framework for the design of a novel concentric tube steerable drilling robot (CT-SDR). The proposed framework leverages a patient-specific finite element (FE) biomechanics model developed based on Quantitative Computed Tomography (QCT) scans of the patient's vertebra to calculate a biomechanically-optimal and feasible drilling and implantation trajectory. The FE output is then used as a design requirement for the design and evaluation of the CT-SDR. Providing a balance between the necessary flexibility to create curved optimal trajectories obtained by the FE module with the required strength to not buckle during drilling through a hard simulated bone material, we showed that the CT-SDR can reliably recreate this drilling trajectory with errors between 1.7-2.2%Comment: 6 pages, 7 figures, Accepted for Publication at the 2023 International Symposium on Medical Robotic