Jove
Visualize
Contact Us

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Displacement Sensing of an Active String Actuator Using a Step-Index Multimode Optical Fiber Sensor.

Sensors (Basel, Switzerland)ยท2022
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 14, 2025

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
00:08

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

Published on: September 30, 2019

6.2K

Length Estimation of Pneumatic Artificial Muscle with Optical Fiber Sensor Using Machine Learning.

Yilei Ni1, Shuichi Wakimoto1, Weihang Tian1

  • 1Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama 700-8530, Japan.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary

Researchers developed a smart artificial muscle with integrated optical fibers for motion sensing. Machine learning, specifically Long Short-Term Memory networks, significantly improved length estimation accuracy by accounting for complex sensor dependencies.

Keywords:
McKibben artificial musclemachine learningmotion estimationoptical fiber

More Related Videos

Electromechanical Assessment of Optogenetically Modulated Cardiomyocyte Activity
12:52

Electromechanical Assessment of Optogenetically Modulated Cardiomyocyte Activity

Published on: March 5, 2020

7.9K
Measurement of Maximum Isometric Force Generated by Permeabilized Skeletal Muscle Fibers
11:30

Measurement of Maximum Isometric Force Generated by Permeabilized Skeletal Muscle Fibers

Published on: June 16, 2015

25.3K

Related Experiment Videos

Last Updated: May 14, 2025

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
00:08

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

Published on: September 30, 2019

6.2K
Electromechanical Assessment of Optogenetically Modulated Cardiomyocyte Activity
12:52

Electromechanical Assessment of Optogenetically Modulated Cardiomyocyte Activity

Published on: March 5, 2020

7.9K
Measurement of Maximum Isometric Force Generated by Permeabilized Skeletal Muscle Fibers
11:30

Measurement of Maximum Isometric Force Generated by Permeabilized Skeletal Muscle Fibers

Published on: June 16, 2015

25.3K

Area of Science:

  • Robotics
  • Materials Science
  • Sensor Technology

Background:

  • McKibben artificial muscles are flexible, lightweight soft actuators.
  • Integrating optical fibers enables motion sensing in these actuators.
  • Traditional methods struggle with sensor dependency on load and pressure.

Purpose of the Study:

  • To develop a smart artificial muscle capable of accurate motion sensing.
  • To overcome limitations in optical fiber sensing due to load and pressure variations.
  • To enhance length measurement accuracy using advanced algorithms.

Main Methods:

  • Integrated an optical fiber into the McKibben artificial muscle sleeve.
  • Measured macrobending and microbending loss in the optical fiber.
  • Employed a machine learning model, primarily Long Short-Term Memory (LSTM) neural networks, for length estimation.

Main Results:

  • Optical fiber sensor characteristics were dependent on length, load, and air pressure.
  • Machine learning effectively compensated for these complex dependencies.
  • LSTM-based length estimation demonstrated significantly reduced error compared to traditional methods.

Conclusions:

  • Machine learning offers a feasible and effective approach to enhance artificial muscle length measurement accuracy.
  • Smart artificial muscles with integrated sensors show promise for advanced robotic applications.
  • LSTM networks provide a robust solution for complex, multi-variable sensor data interpretation.