Machine-Learning-Enabled Multimode Fiber Specklegram Sensors: A Review

Multimode fiber (MMF) specklegram sensors have recently drawn significant attention due to the incorporation of machine learning (ML) algorithms in detecting different sensing parameters. Deep learning (DL) techniques provide an efficient way of extracting information from fiber specklegrams, which can be used in different sensing applications. Convolutional neural networks (CNNs) have proven to be extremely successful in imaging technologies over the past decade. The breakthrough from CNN has instigated new frontiers for applications in different domains. CNNs can automatically learn the variations in MMF specklegrams under different conditions. Besides detecting slight variations in the fiber, CNNs are also insusceptible to environmental noise and fluctuations, thus making them superior in terms of performance accuracy. They provide a low-cost and simple alternative to extracting information from fiber specklegrams which has piqued the interest of many researchers. In the past few years, there have been a growing number of research articles studying the applicability of DL frameworks in various sensing technologies. In this article, we present a comprehensive review of such articles that explore the use of ML in different MMF sensing applications, such as bending sensors, endoscopes, tactile or position sensors, and others. The principle of specklegram in MMF, the data generation process, an overview of different DL approaches, and open challenges and future research directions have also been discussed in this article.