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

Kun Wang, Yosuke Mizuno, Xingchen Dong, Wolfgang Kurz, Michael Köhler, Patrick Kienle, Heeyoung Lee, Martin Jakobi, Alexander W. Koch

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations


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.

Original languageEnglish
Article number022002
JournalMeasurement Science and Technology
Issue number2
StatePublished - Feb 2024
Externally publishedYes


  • machine learning
  • multimode fiber
  • optical fiber sensor


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