Abstract Multimode fiber (MMF) sensors have been extensively developed and utilized in various sensing applications for decades. Traditionally, the performance of MMF sensors was improved by conventional methods that focused on structural design and specialty fibers. However, in recent years, the blossom of machine learning techniques has opened up new avenues for enhancing the performance of MMF sensors. Unlike conventional methods, machine learning techniques do not require complex structures or rare specialty fibers, which reduces fabrication difficulties and lowers costs. In this review, we provide an overview of the latest developments in MMF sensors, ranging from conventional methods to those assisted by machine learning. This article begins by categorizing MMF sensors based on their sensing applications, including temperature and strain sensors, displacement sensors, refractive index sensors, curvature sensors, bio/chemical sensors, and other sensors. Their distinct sensor structures and sensing properties are thoroughly reviewed. Subsequently, the machine learning-assisted MMF sensors that have been recently reported are analyzed and categorized into two groups: learning the specklegrams and learning the spectra. The review provides a comprehensive discussion and outlook on MMF sensors, concluding that they are expected to be utilized in a wide range of applications.

Kun Wang

Yosuke Mizuno

Xingchen Dong

Wolfgang Kurz

Michael Köhler

Patrick Kienle

Heeyoung Lee

Martin Jakobi

Alexander W Koch

Measurement Science and Technology

测量科学与技术发表关于新测量技术和相关仪器的文章。描述实验的论文必须代表测量科学或测量技术的进步，而不是已建立的实验技术的应用。考虑到期刊的多学科性质，作者必须对其工作进行介绍，以明确其工作在测量环境中的新颖性、重要性、更广泛的相关性以及与测量科学与技术读者群的相关性。所有提交的文章都应考虑所提供的测量的不确定性、精度和/或准确性。主题范围包括从开始到商业开发的物理、化学、工程以及环境和生命科学中测量的理论、实践和应用。期刊中的出版物应强调报告方法的新颖性，对其进行表征并使用示例或应用程序展示其性能。

The journal "Measurement Science and Technology" publishes research articles on new measurement technologies and related instruments. All experimental papers must demonstrate advancements in measurement science or technology, rather than merely applying established experimental techniques. Given the multidisciplinary nature of the journal, authors are required to clearly articulate the novelty, significance, broader relevance, and connection of their work to the readership of "Measurement Science and Technology". All submitted articles should consider the uncertainty, precision, and/or accuracy of the measurements provided. The scope of the journal includes the theory, practice, and application of measurements in physics, chemistry, engineering, environmental, and life sciences, ranging from fundamental research to commercial development. Publications in the journal should emphasize the novelty of the reported methods and demonstrate their performance through examples or applications.

《Measurement Science and Technology》期刊发表关于新测量技术及相关仪器的研究文章。所有描述实验的论文必须展示测量科学或测量技术的进步，而不仅仅是已建立实验技术的应用。考虑到期刊的多学科性质，作者需清晰阐述其研究在测量领域中的新颖性、重要性及广泛相关性，以及与《Measurement Science and Technology》读者群的关联性。所有提交的文章都应关注所提供测量的不确定性、精度和/或准确性。期刊涵盖的主题范围包括从基础研究到商业开发的物理、化学、工程、环境及生命科学中的测量理论、实践与应用。期刊中的出版物应强调方法的新颖性，并通过示例或应用展示其性能。

Multimode optical fiber sensors: from conventional to machine learning-assisted

An overview of the latest developments in MMF sensors, ranging from conventional methods to those assisted by machine learning, is provided, concluding that they are expected to be utilized in a wide range of applications.

Category	Quartile
工程技术	3
工程技术, 工程综合	3
工程技术, 仪器仪表	3

Multimode optical fiber sensors: from conventional to machine learning-assisted

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Journal Info

Category	Quartile
INSTRUMENTS & INSTRUMENTATION	2