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Related Experiment Video

Updated: Jan 15, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Enhancing open-surgery gesture recognition using 3D pose estimation.

Ori Meiraz¹, Shlomi Laufer², Robert Spector²

¹Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Technion City, 32000, Haifa, Israel. ori.meiraz@campus.technion.ac.il.

International Journal of Computer Assisted Radiology and Surgery

|January 14, 2026

Summary

This summary is machine-generated.

This study enhances surgical gesture recognition using multimodal data, combining video, hand, and tool poses for open surgery. Pose-only models offer a privacy-preserving alternative with comparable accuracy to video-based methods.

Keywords:

Action recognition Gesture recognition Open surgery Pose estimation

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Area of Science:

Medical Robotics
Computer Vision
Surgical Data Science

Background:

Surgical gesture recognition is crucial for analyzing surgical procedures.
Existing methods often use simulations or minimally invasive data, not reflecting open surgery complexities.
Open surgery datasets are limited, hindering progress in gesture recognition.

Purpose of the Study:

To introduce and utilize a novel open surgery dataset for gesture recognition.
To improve surgical gesture recognition accuracy by integrating tool and hand pose estimation.
To evaluate multimodal approaches for enhanced performance in open surgery contexts.

Main Methods:

Developed a new open surgery dataset for saphenous vein harvesting incision closure.
Employed MS-TCN++ and LTContext models for gesture recognition.
Utilized an ensemble of models combining video, tool pose, and hand pose data.

Main Results:

Ensemble models integrating all three modalities significantly outperformed video-only approaches.
Models relying solely on hand and tool poses achieved comparable performance to video-based methods.
Pose-only models demonstrated statistically significant or comparable results to video-only methods.

Conclusions:

Integrating multimodal data (video, hand pose, tool pose) enhances surgical gesture recognition accuracy and robustness.
Pose-only models present a viable, privacy-preserving alternative for surgical data analysis.
This research advances automated analysis of open surgical procedures.