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Related Experiment Video

Updated: Nov 20, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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Gesture Recognition in Robotic Surgery: A Review.

Beatrice van Amsterdam, Matthew J Clarkson, Danail Stoyanov

    IEEE Transactions on Bio-Medical Engineering
    |January 26, 2021
    PubMed
    Summary

    This review analyzes robotic surgery gesture recognition, finding deep learning shows promise on small datasets. Large, annotated datasets are crucial for robust, fine-grained surgical action recognition development.

    Area of Science:

    • Robotics
    • Computer-assisted interventions
    • Surgical analytics

    Background:

    • Surgical activity recognition is key for computer-assisted interventions.
    • Fine-grained gesture recognition in robotic surgery is an evolving field.
    • Data-driven approaches are increasingly important.

    Purpose of the Study:

    • To review state-of-the-art methods for automatic recognition of fine-grained gestures in robotic surgery.
    • To focus on recent data-driven approaches.
    • To outline open questions and future research directions.

    Main Methods:

    • A systematic literature search was conducted across 5 bibliographic databases.
    • Search terms included "robotic", "surgery", "gesture recognition", and "surgeme".

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  • Selected articles were classified by supervision level and data modeling frameworks.

    • Main Results:

    • 52 articles were reviewed, with most published in the last 4 years.
    • Deep-learning models show promise on small surgical datasets.
    • Supervised methods currently outperform unsupervised approaches.

    Conclusions:

    • Development of large, annotated, open-source datasets is essential for robust surgical gesture recognition.
    • Unsupervised and semi-supervised methods show potential but haven't matched supervised performance.
    • Future research should focus on error/anomaly detection and forecasting.