Their article “Optical lace for synthetic afferent neural networks” published in Science Robotics describes various experiments using a 3D-printer to integrate arbitrary 3D grids of soft stretchable light guides throughout the volume of a soft deformable scaffold. Some of the light guides are considered as input cores (receiving light from a LED) while networks of neighboring light guides act as output cores, only carrying leaked light from the input cores upon deformation.
By optically measuring the coupling interactions (all the light power outputs), they were able to sense spatially continuous deformations and localize them with sub-millimeter accuracy, detecting forces as low as 0.3 Newton. In one experiment, they were able to simultaneously locate multiple finger presses and monitor the volumetric structural deformation of a soft scaffold just by measuring the coupling interactions within the optical lace.
Here they used 1.5mm diameter polyurethane light guides for the input cores and 1mm diameter light guides for the output cores, loosely held in place into lattice-work channels designed within the scaffold structure and spaced apart by small air gaps in the resting state. For any given finger press, the touch position could be calculated using the ratio of intensities in the neighboring output light guides.