Jove
Visualize
Contact Us
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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Dexterous Manipulation Based on Object Recognition and Accurate Pose Estimation Using RGB-D Data.

Sensors (Basel, Switzerland)·2024
Same author

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks.

Sensors (Basel, Switzerland)·2023
Same author

Pedestrian Crossing Sensing Based on Hough Space Analysis to Support Visually Impaired Pedestrians.

Sensors (Basel, Switzerland)·2023
Same author

Cyclist Orientation Estimation Using LiDAR Data.

Sensors (Basel, Switzerland)·2023
Same author

High Precision Location Estimation in Mountainous Areas Using GPS.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Aug 10, 2025

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies
10:50

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Published on: November 8, 2018

10.9K

Altering Fish Behavior by Sensing Swarm Patterns of Fish in an Artificial Aquatic Environment Using an Interactive

Udaka A Manawadu1, Malsha De Zoysa2, J D H S Perera2

  • 1Graduate School of Computer Science and Engineering, University of Aizu, Fukushima 965-0006, Japan.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary

This study introduces a robotic fish to control fish movement for enhanced human interaction in aquatic environments. The robotic fish successfully altered fish behavior, improving user engagement and therapeutic experiences.

Keywords:
fish image processingmotion detectionostraciiform fishrobotic fishtrackingunderwater robotics

More Related Videos

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

12.6K
Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish
14:03

Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish

Published on: December 5, 2013

11.1K

Related Experiment Videos

Last Updated: Aug 10, 2025

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies
10:50

Using the FishSim Animation Toolchain to Investigate Fish Behavior: A Case Study on Mate-Choice Copying In Sailfin Mollies

Published on: November 8, 2018

10.9K
Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
10:56

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

Published on: March 6, 2014

12.6K
Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish
14:03

Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish

Published on: December 5, 2013

11.1K

Area of Science:

  • Robotics
  • Biomedical Engineering
  • Computer Science

Background:

  • Aquatic environments offer calming and stress-reducing effects.
  • Institutions use fish for entertainment and patient therapy.
  • Controlling fish movement for human interaction remains a challenge.

Purpose of the Study:

  • To develop an interactive robotic fish to influence fish swarm behavior.
  • To enhance human-fish interaction through controlled robotic maneuvers.
  • To create a system for seamless integration of robotic and natural fish behaviors.

Main Methods:

  • Designed a futuristic robotic fish with cameras and infrared sensors.
  • Developed a fish-detecting algorithm using background subtraction and moving average (78% accuracy).
  • Implemented a swarm pattern-recognizing algorithm using Convolutional Neural Networks (CNN) (77.32% accuracy).

Main Results:

  • The robotic fish effectively altered fish swarm behavior.
  • Controlled fish movements enhanced human interaction.
  • Repeated trials and subject feedback confirmed improved engagement.

Conclusions:

  • The robotic fish successfully manipulated fish behavior to improve human interaction.
  • This technology has potential applications in therapy and entertainment.
  • The system demonstrates a novel approach to human-animal interaction in aquatic settings.