Learning to sense three-dimensional shape deformation of a single multimode fiber.
Learning to sense three-dimensional shape deformation of a single multimode fiber.
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DOI：
10.1038/s41598-022-15781-8
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发表时间：
2022-07-25
影响因子：
4.6
通讯作者：
Su, Lei
中科院分区：
文献类型：
--
作者：
Wang, Xuechun;Wang, Yufei;Zhang, Ketao;Althoefer, Kaspar;Su, Lei
光纤弯曲、变形或形状传感是重要的测量技术，已广泛应用于医疗保健、结构监测和机器人等各种应用中。然而，现有的光纤弯曲传感器需要复杂的传感器结构和询问系统。在这里，受到最近对信息丰富的多模光纤重新产生的兴趣的启发，我们表明多模光纤 (MMF) 输出散斑包含 MMF 本身的三维 (3D) 几何形状信息。我们使用 k 最近邻 (KNN) 机器学习算法演示了通过单多模光纤进行的 3D 多点变形传感概念验证，并实现了接近 100% 的分类精度。我们的结果表明，基于单个 MMF 的变形传感器在系统简单性、分辨率和灵敏度方面非常出色，并且可以成为变形监测或形状传感应用的有前途的候选者。
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.
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