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Learning to sense three-dimensional shape deformation of a single multimode fiber.

ABSTRACT: Optical fiber bending, deformation or shape sensing are important measurement technologies and have been widely deployed in various applications including healthcare, structural monitoring and robotics. However, existing optical fiber bending sensors require complex sensor structures and interrogation systems. Here, inspired by the recent renewed interest in information-rich multimode optical fibers, we show that the multimode fiber (MMF) output speckles contain the three-dimensional (3D) geometric shape information of the MMF itself. We demonstrate proof-of-concept 3D multi-point deformation sensing via a single multimode fiber by using k-nearest neighbor (KNN) machine learning algorithm, and achieve a classification accuracy close to 100%. Our results show that a single MMF based deformation sensor is excellent in terms of system simplicity, resolution and sensitivity, and can be a promising candidate in deformation monitoring or shape-sensing applications.

SUBMITTER: Wang X

PROVIDER: S-EPMC9314325 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Learning to sense three-dimensional shape deformation of a single multimode fiber.

Wang Xuechun X Wang Yufei Y Zhang Ketao K Althoefer Kaspar K Su Lei L

Scientific reports 20220725 1

Optical fiber bending, deformation or shape sensing are important measurement technologies and have been widely deployed in various applications including healthcare, structural monitoring and robotics. However, existing optical fiber bending sensors require complex sensor structures and interrogation systems. Here, inspired by the recent renewed interest in information-rich multimode optical fibers, we show that the multimode fiber (MMF) output speckles contain the three-dimensional (3D) geomet ...[more]

PMID: 35879319

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