Deep Learning-Enabled Two-Directional Stretchable Strain Sensor Based on a Single Multimode Fiber

Abstract

In this work, we have demonstrated a specklegram proprioceptive sensor capable of sensing large tensile strains based on a single silica multimode fiber (MMF) embedded in a soft silicone pad. Taking advantage of the rich information contained in the output speckle patterns from the MMF and a convolutional neural network-based regression demodulation algorithm, the sensor shows an extended strain sensing range up to 10%, and the root mean squared error is determined to be less than 0.05%. Additionally, by integrating with a classification model, the sensor is also able to distinguish the direction of the applied tensile strains, i.e., the axial or the longitudinal directions, with an accuracy of 100%. The proposed sensor has the advantages of low cost, ease of fabrication, and large strain capability, and could find wide applications in the field of soft robotics.