Fabrication and Characterization of Additively Manufactured Stretchable Strain Sensors Towards the Shape Sensing of Continuum Robots

This letter describes the manufacturing and experimental characterization of novel stretchable strain sensors for continuum robots. The overarching goal of this research is to provide a new solution for the shape sensing of these devices. The sensors are fabricated via direct ink writing, an extrusion-based additive manufacturing technique. Electrically conductive material (i.e., the \textit{ink}) is printed into traces whose electrical resistance varies in response to mechanical deformation. The principle of operation of stretchable strain sensors is analogous to that of conventional strain gauges, but with a significantly larger operational window thanks to their ability to withstand larger strain. Among the different conductive materials considered for this study, we opted to fabricate the sensors with a high-viscosity eutectic Gallium-Indium ink, which in initial testing exhibited high linearity ($R^2 \approx$ 0.99), gauge factor $\approx$ 1, and negligible drift. Benefits of the proposed sensors include (i) ease of fabrication, as they can be conveniently printed in a matter of minutes; (ii) ease of installation, as they can simply be glued to the outside body of a robot; (iii) ease of miniaturization, which enables integration into millimiter-sized continuum robots.

Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI

Diffusion MRI (dMRI) streamline tractography, the gold standard for in vivo estimation of brain white matter (WM) pathways, has long been considered indicative of macroscopic relationships with WM microstructure. However, recent advances in tractography demonstrated that convolutional recurrent neural networks (CoRNN) trained with a teacher-student framework have the ability to learn and propagate streamlines directly from T1 and anatomical contexts. Training for this network has previously relied on high-resolution dMRI. In this paper, we generalize the training mechanism to traditional clinical resolution data, which allows generalizability across sensitive and susceptible study populations. We train CoRNN on a small subset of the Baltimore Longitudinal Study of Aging (BLSA), which better resembles clinical protocols. Then, we define a metric, termed the epsilon ball seeding method, to compare T1 tractography and traditional diffusion tractography at the streamline level. Under this metric, T1 tractography generated by CoRNN reproduces diffusion tractography with approximately two millimeters of error.