Development of a Fiberoptic Distributive Shear/Pressure Sensor

An estimate of $6 billion is being spent annually to deal with diabetes-related health problems. In the United States, 6% of the population (about 18 million people) has diabetes, either diagnosed or undiagnosed. During their lifetimes, about 15% of diabetic patients will develop a foot ulcer. More than 60,000 of them will end up with amputations of lower extremities due to complications arising from diabetes mellitus, accounting for nearly two-thirds of the total number of nontraumatic amputations performed annually. Previous studies report that has indicated that both the vertical and shear components of ground reaction force play a role in the formation of plantar ulcers. However, the exact relationship between stresses and ulcer formation is still unclear. The reason for that is due to lack of a sensor that is currently available to be able to measure pressure and shear force on the plantar surface simultaneously. It is because of these inadequacies that have motivated the search for a new design to transduce pressure and shear stress based on a multi-layered optical bend loss sensor. We have recently developed a sensor that can be used to measure shear and pressure of an extended area using fiber optic technology. The fiber optic technique that we have developed has also been extended by utilizing an array of microfabricated waveguides made of polydimethylsiloxane (PDMS). For the fiberoptic technique, the pressure/shear sensor consisted of an array of optical fibers lying in perpendicular rows and columns separated by elastomeric pads. A map of pressure and shear stress was constructed based on observed macro bending through the intensity attenuation from the physical deformation of two adjacent perpendicular fibers. Each sensing layer consists of multiple fibers molded into a thin PDMS substrate. In this design, the top layer is composed of a 3 by 3 fiber mesh with 9 intersection points and the bottom layer is made of a 4 by 4 fiber mesh with 16 intersection points. The space between the adjacent fibers is 0.5 cm. The pressured points between the top and the bottom layer are offset by 0.25 cm which is used to increase the shear sensitivity. In the experiment, the sensor was tested with various loading condition. A force image algorithm using neural networks was developed to identify the loading pattern, magnitude and direction of loads that were applied at more than one pressure point. Here 3 loading patterns with 5 different loading directions and 3 different loading magnitudes were tested. The results show a >90% accuracy was obtained using an algorithm with 2 layered neural networks system.