Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Local Attraction

Local Attraction

Local attraction refers to disturbances in compass readings caused by magnetic influences from nearby objects such as metal fences, buried pipes, vehicles, buildings, power lines, or natural iron ore deposits. Small items like wristwatches, steel tools, or belt buckles can also interfere with the compass by creating local magnetic fields that distort the Earth's natural magnetic field. These distortions lead to inaccurate readings, posing navigation and land surveying challenges.Local...

Magnetism

Magnetism

Magnets are commonly found in everyday objects, such as toys, hangers, elevators, doorbells, and computer devices. Experimentation on these magnets shows that all magnets have two poles: one is labeled north (N) and the other south (S). Magnetic poles repel if they are alike and attract if unlike. Moreover, both poles of a magnet attract unmagnetized pieces of iron.
An individual magnetic pole cannot be isolated. No matter how small, every piece of a magnet contains a north pole and a south...

You might also read

Related Articles

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

Sort by

Same author

Toward autonomous robotic-assisted and microrobotic surgery.

Science advances·2026

Same author

Tissue removal inside the beating heart using a robotically delivered metal MEMS tool.

The International journal of robotics research·2026

Same author

Schisandrin B exhibits anti‑proliferative effects by inducing ferroptosis in pancreatic cancer.

Oncology reports·2026

Same authorSame journal

Using Robotics to Improve Transcatheter Edge-to-Edge Repair of the Mitral Valve.

IEEE robotics and automation letters·2026

Same author

Cross-Continental Differences in Climate Asymmetrically Shape Temporal Ecological Processes in Riverine Insect Diversity.

Global change biology·2026

Same author

Risk factors for intraoperative hypothermia in patients undergoing thoracic spinal surgery: A retrospective cohort study.

The Journal of international medical research·2026

Same journal

Value Explicit Pretraining for Learning Transferable Representations.

IEEE robotics and automation letters·2026

Same journal

Continuum Robot Segments with High Output Stiffness via Diagonal Backbones.

IEEE robotics and automation letters·2026

Same journal

Friction Modeling of Tendon-driven Continuum Robots through Linear Complementarity Problem.

IEEE robotics and automation letters·2026

Same journal

Efficient and Scalable Tuning of Continuous Impedance Control for Powered Knee Prostheses.

IEEE robotics and automation letters·2026

Same journal

Validation of Dynamic Bayesian Optimization for a Non-Stationary Human-in-the-Loop Optimization Problem.

IEEE robotics and automation letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

Localizing Function-specific Targets for Transcranial Magnetic Stimulation in the Absence of Navigation Equipment

Localizing Function-specific Targets for Transcranial Magnetic Stimulation in the Absence of Navigation Equipment

Published on: May 23, 2025

Few-Shot Magnet Localization Using Sim-to-Real Transfer Learning.

Yuan Wang¹, Giovanni Pittiglio², Xiaowei Lin¹

¹Department of Cardiac Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.

IEEE Robotics and Automation Letters

|November 17, 2025

Summary

This summary is machine-generated.

This study introduces a novel learning-based magnetic localization method for real-time medical instrument tracking. The new approach significantly improves accuracy and efficiency compared to traditional physics-model-based methods.

Keywords:

Magnetic localization cardiac catheter few-shot learning neural networks

More Related Videos

Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures

Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures

Published on: April 21, 2013

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Related Experiment Videos

Last Updated: Jan 11, 2026

Localizing Function-specific Targets for Transcranial Magnetic Stimulation in the Absence of Navigation Equipment

Localizing Function-specific Targets for Transcranial Magnetic Stimulation in the Absence of Navigation Equipment

Published on: May 23, 2025

Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures

Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures

Published on: April 21, 2013

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Medical instrumentation
Machine learning in healthcare
Biomedical engineering

Background:

Magnetic localization is a key radiation-free technology for real-time medical instrument tracking.
Current physics-model-based methods face limitations in accuracy due to imperfect assumptions and computational demands.
Need for more efficient and accurate localization techniques in medical procedures.

Purpose of the Study:

To develop an efficient and accurate learning-based magnetic localization method.
To achieve low computation cost and few-shot generalization for magnetic tracking.
To demonstrate the practical efficacy of the proposed method in medical applications.

Main Methods:

A single, compact neural network was designed for magnetic localization.
Transfer learning was employed using a simulation dataset for few-shot generalization.
The method was evaluated using four 3-channel sensors within a 100×100×100mm workspace.

Main Results:

Achieved mean position error of 1.66 mm and orientation error of 2.15°.
Demonstrated a 2x improvement in accuracy compared to traditional magnetic model-based methods.
Successfully validated in a real-time catheter tracking experiment.

Conclusions:

The proposed learning-based method offers efficient and accurate magnetic localization.
This technique provides a significant advancement over existing physics-model-based approaches.
The method shows strong potential for practical applications in real-time medical instrument tracking.