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Related Experiment Video

Updated: Jun 27, 2026

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing
10:52

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing

Published on: March 8, 2020

A Dual-FBG Sensor with Machine Learning for Microstrain-Temperature Decoupling Under Cyanoacrylate Bonding Toward

Sung-Ho Yang1, Cheng-Kai Yao1, Amare Mulatie Dehnaw1

  • 1Department of Electro-Optical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Micromachines
|June 26, 2026
PubMed
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This study introduces a novel dual-Fiber Bragg Grating (FBG) sensor system enhanced with machine learning. It accurately measures strain and temperature in cardiovascular catheters, improving intervention safety and efficacy.

Area of Science:

  • Biomedical Engineering
  • Optical Sensing Technologies
  • Machine Learning Applications

Background:

  • Accurate real-time monitoring of strain and temperature at the catheter tip is crucial for cardiovascular interventions.
  • Fiber Bragg Grating (FBG) sensors offer miniaturization and electromagnetic immunity but suffer from strain-temperature cross-sensitivity.
  • Existing methods struggle to precisely differentiate between strain and temperature signals in complex environments.

Purpose of the Study:

  • To develop a dual-FBG fiber optic sensing structure integrated with machine learning for precise, decoupled strain and temperature measurement.
  • To overcome the cross-sensitivity limitations of traditional FBG sensors in catheter-based applications.
  • To enhance the safety and efficacy of cardiovascular interventional procedures through advanced sensing capabilities.
Keywords:
catheter systemcross-sensitivity compensationdual-FBG sensing structurefiber Bragg gratingmachine learningstrain and temperature measurement

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Related Experiment Videos

Last Updated: Jun 27, 2026

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing
10:52

Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing

Published on: March 8, 2020

Thermal Measurement Techniques in Analytical Microfluidic Devices
08:29

Thermal Measurement Techniques in Analytical Microfluidic Devices

Published on: June 3, 2015

Optimized Sealing Process and Real-Time Monitoring of Glass-to-Metal Seal Structures
04:41

Optimized Sealing Process and Real-Time Monitoring of Glass-to-Metal Seal Structures

Published on: September 2, 2019

Main Methods:

  • A dual-FBG fiber optic sensing structure was designed, with one FBG set measuring composite signals and a second set measuring only temperature.
  • A machine learning model was trained to learn the nonlinear relationship between FBG spectral data and physical strain/temperature parameters.
  • Experimental validation was performed within a physiological temperature range (20 °C to 45 °C) using a simulated catheter setup.

Main Results:

  • The machine learning model successfully decoupled strain and temperature signals with high precision.
  • Accurate predictions were achieved even with slight nonlinear responses from the adhesive bonding of the sensor.
  • The system demonstrated robustness in distinguishing between strain and temperature fluctuations.

Conclusions:

  • The proposed machine learning-enhanced dual-FBG structure is feasible for multi-parameter sensing in challenging environments.
  • This technology offers a promising pathway for developing next-generation smart optical fiber sensors for catheter systems.
  • The methodology significantly advances real-time monitoring capabilities for cardiovascular interventions.