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

Active physical human-exoskeleton interaction based on motion intention adaptive recognition and synchronous tracking.

Frontiers in neurorobotics·2026
Same author

Multimodal information-fused embodied intelligence toward autonomous robotic microsurgery.

Innovation (Cambridge (Mass.))·2026
Same author

A Comprehensive Framework for Generating Adaptive Arbitrarily Predefined-Time Convergent RNNs for Dynamic Zero-Finding Problem Applied to Circuits and Robotics.

IEEE transactions on cybernetics·2026
Same author

Enhanced fNIRS-Based MCI Detection via Resting-State and Task-State Integration With Spatial-Temporal Feature Reduction.

IEEE journal of translational engineering in health and medicine·2026
Same author

FasterEEG: Adaptive Channel and Model Size Optimization for Efficient Brain Decoding.

IEEE transactions on bio-medical engineering·2026
Same author

Autonomous robotic intraocular surgery for targeted retinal injections.

Science robotics·2026

Related Experiment Video

Updated: Feb 20, 2026

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

1.2K

An HMM-based recognition framework for endovascular manipulations.

Xiao-Hu Zhou, Gui-Bin Bian, Xiao-Liang Xie

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary

    This study introduces a Hidden Markov Model (HMM)-based framework to analyze surgical skills in endovascular manipulations. The system accurately recognizes key techniques, aiding the development of next-generation vascular interventional robots.

    More Related Videos

    Simulator Training for Endovascular Neurosurgery
    08:08

    Simulator Training for Endovascular Neurosurgery

    Published on: May 6, 2020

    4.2K
    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
    06:18

    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

    Published on: April 5, 2024

    1.6K

    Related Experiment Videos

    Last Updated: Feb 20, 2026

    Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
    07:46

    Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

    Published on: August 9, 2024

    1.2K
    Simulator Training for Endovascular Neurosurgery
    08:08

    Simulator Training for Endovascular Neurosurgery

    Published on: May 6, 2020

    4.2K
    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
    06:18

    Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

    Published on: April 5, 2024

    1.6K

    Area of Science:

    • Robotics
    • Cardiovascular Surgery
    • Medical Simulation

    Background:

    • Robotic surgical systems are increasingly used for cardiovascular diseases.
    • Current systems often neglect natural surgical manipulation techniques crucial for percutaneous coronary intervention success.

    Purpose of the Study:

    • To propose a Hidden Markov Model (HMM)-based framework for recognizing six typical endovascular manipulations.
    • To enable objective surgical skill analysis in endovascular procedures.

    Main Methods:

    • Development of an HMM-based framework for manipulation recognition.
    • Construction of a simulative surgical platform for data acquisition.
    • Assessment of the framework's performance with five subjects (1 expert, 4 novices) using three experimental schemes.

    Main Results:

    • High accuracy and reliable performance in recognizing endovascular manipulations.
    • Demonstrated effectiveness of the HMM-based framework for surgical skill analysis.

    Conclusions:

    • The proposed framework accurately recognizes endovascular manipulations, providing valuable insights for surgical skill assessment.
    • The findings support the design of improved vascular interventional robots by incorporating natural manipulation techniques.