ExoNav II: Design of a Robotic Tool with Follow-the-Leader Motion Capability for Lateral and Ventral Spinal Cord Stimulation (SCS)

Spinal cord stimulation (SCS) electrodes are traditionally placed in the dorsal epidural space to stimulate the dorsal column fibers for pain therapy. Recently, SCS has gained attention in restoring gait. However, the motor fibers triggering locomotion are located in the ventral and lateral spinal cord. Currently, SCS electrodes are steered manually, making it difficult to navigate them to the lateral and ventral motor fibers in the spinal cord. In this work, we propose a helically micro-machined continuum robot that can bend in a helical shape when subjected to actuation tendon forces. Using a stiff outer tube and adding translational and rotational degrees of freedom, this helical continuum robot can perform follow-the-leader (FTL) motion. We propose a kinematic model to relate tendon stroke and geometric parameters of the robot's helical shape to its acquired trajectory and end-effector position. We evaluate the proposed kinematic model and the robot's FTL motion capability experimentally. The stroke-based method, which links tendon stroke values to the robot's shape, showed inaccuracies with a 19.84 mm deviation and an RMSE of 14.42 mm for 63.6 mm of robot's length bending. The position-based method, using kinematic equations to map joint space to task space, performed better with a 10.54 mm deviation and an RMSE of 8.04 mm. Follow-the-leader experiments showed deviations of 11.24 mm and 7.32 mm, with RMSE values of 8.67 mm and 5.18 mm for the stroke-based and position-based methods, respectively. Furthermore, end-effector trajectories in two FTL motion trials are compared to confirm the robot's repeatable behavior. Finally, we demonstrate the robot's operation on a 3D-printed spinal cord phantom model.

An FBG-based Stiffness Estimation Sensor for In-vivo Diagnostics

In-vivo tissue stiffness identification can be useful in pulmonary fibrosis diagnostics and minimally invasive tumor identification, among many other applications. In this work, we propose a palpation-based method for tissue stiffness estimation that uses a sensorized beam buckled onto the surface of a tissue. Fiber Bragg Gratings (FBGs) are used in our sensor as a shape-estimation modality to get real-time beam shape, even while the device is not visually monitored. A mechanical model is developed to predict the behavior of a buckling beam and is validated using finite element analysis and bench-top testing with phantom tissue samples (made of PDMS and PA-Gel). Bench-top estimations were conducted and the results were compared with the actual stiffness values. Mean RMSE and standard deviation (from the actual stiffnesses) values of 413.86 KPa and 313.82 KPa were obtained. Estimations for softer samples were relatively closer to the actual values. Ultimately, we used the stiffness sensor within a mock concentric tube robot as a demonstration of \textit{in-vivo} sensor feasibility. Bench-top trials with and without the robot demonstrate the effectiveness of this unique sensing modality in \textit{in-vivo} applications.