Spatially-resolved bending recognition based on a learning-empowered fiber specklegram sensor

Fiber specklegram sensors do not rely on complex fabrication processes and expensive sensor interrogation schemes and provide an alternative to routinely used fiber sensing technologies. Most of the reported specklegram demodulation schemes focus on correlation calculation based on statistical properties or classification according to features, resulting in limited measurement range and resolution. In this work, we propose and demonstrate a learning-empowered spatially resolved method for fiber specklegram bending sensors. This method can learn the evolution process of speckle patterns through a hybrid framework constructed by a data dimension reduction algorithm and regression neural network, which can simultaneously identify the curvature and perturbed position according to the specklegram, even for the unlearned curvature configuration. Rigorous experiments are performed to verify the feasibility and robustness of the proposed scheme, and the results show that the prediction accuracy for the perturbed position is 100%, and the average prediction errors for the curvature of the learned and unlearned configurations are 7.79 × 10^-4m^-1 and 7.02 × 10^-2m^-1, respectively. The proposed method promotes the application of fiber specklegram sensors in the practical scene and provides insights for the interrogation of sensing signals by deep learning.