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Related Experiment Video

Updated: Jul 23, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Surgical Instrument Signaling Gesture Recognition Using Surface Electromyography Signals.

Melissa La Banca Freitas1, José Jair Alves Mendes2, Thiago Simões Dias2

  • 1Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology-Paraná (UTFPR), Ponta Grossa 84017-220, PR, Brazil.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a wearable system for Surgical Instrument Signaling (SIS) using surface electromyography (sEMG) signals. The system achieved 88% accuracy in recognizing 14 distinct gestures, enhancing surgical communication.

Keywords:
automatic segmentationensemble classificationpattern recognitionrobotic surgerysignal processingsurface electromyographytelesurgery

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Robotics

Background:

  • Surgical Instrument Signaling (SIS) relies on hand gestures for surgeon-instrumentator communication, crucial for preventing errors.
  • Existing SIS systems often use limited gesture sets, potentially hindering nuanced communication.
  • Wearable technology offers a promising avenue for real-time, non-invasive surgical communication systems.

Purpose of the Study:

  • To assess the feasibility of a gesture recognition system for SIS using surface electromyographic (sEMG) signals.
  • To develop and evaluate a processing routine for wearable SIS applications, particularly for telesurgery and robotic surgery.
  • To create a comprehensive database of 14 SIS gestures from 10 volunteers for robust system training and testing.

Main Methods:

  • Acquisition of sEMG signals from the Myo armband during the execution of 14 distinct SIS gestures by 10 volunteers.
  • Implementation of a processing routine involving automatic segmentation, feature extraction (13 sets), and feature selection.
  • Classification of sEMG signals using 6 different classifiers, including Support Vector Machine (SVM), and 2 ensemble techniques.

Main Results:

  • An accuracy of 76% was achieved using the SVM classifier across all recorded databases.
  • Individual volunteer analysis yielded a higher accuracy of 88% with the SVM classifier.
  • The developed system demonstrated suitability for real-time SIS gesture recognition in wearable applications.

Conclusions:

  • Surface electromyography (sEMG) signals are effective for recognizing a diverse set of Surgical Instrument Signaling (SIS) gestures.
  • The proposed processing routine and classification methods are suitable for wearable SIS applications, enhancing surgical communication.
  • This research provides a foundation for integrating advanced gesture recognition into telesurgery and robotic surgery systems.