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Related Experiment Video

Updated: Jan 7, 2026

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Localized Muscular Fatigue in Robotic-Assisted Laparoscopic Surgery: Predictive Modeling Study.

Daniel Caballero1, Manuel J Pérez-Salazar1, Juan A Sánchez-Margallo1

  • 1Bioengineering and Health Technologies Unit, Jesús Usón Minimally Invasive Surgery Centre, Cáceres, Cáceres, Spain.

JMIR Formative Research
|December 10, 2025
PubMed
Summary

This study developed a predictive model using electromyography (EMG) to assess muscle fatigue in robotic-assisted surgery (RAS) and laparoscopic surgery (LAP). The model accurately predicts localized muscle fatigue, aiding in improving surgeon ergonomics during minimally invasive surgery (MIS).

Keywords:
artificial intelligenceelectromyographylaparoscopic surgerylocalized muscle fatiguepredictive techniquesrobotic-assisted surgery

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Area of Science:

  • Surgical Technology
  • Biomedical Engineering
  • Ergonomics

Background:

  • Minimally invasive surgery (MIS) presents ergonomic challenges for surgeons despite its widespread adoption.
  • Robotic-assisted surgery (RAS) is increasingly standard, but surgeon fatigue remains a concern.
  • Wearable technology and AI offer potential solutions for analyzing and enhancing surgical ergonomics.

Purpose of the Study:

  • To develop and validate a predictive model for localized muscle fatigue.
  • To analyze muscle fatigue during conventional laparoscopic surgery (LAP) and RAS.
  • To utilize electromyography (EMG) data for fatigue prediction.

Main Methods:

  • Four surgical tasks (dissection, labyrinth, peg transfer, suturing) were performed in LAP and RAS settings.
  • Wireless EMG sensors recorded muscle activity, with data analyzed for localized muscle fatigue.
  • Multiple linear regression (MLR) and multilayer perceptron (MLP) models were trained and validated using surgeon expertise and surgical type data.

Main Results:

  • RAS demonstrated reduced muscle fatigue in novice surgeons compared to LAP, but increased fatigue in expert surgeons.
  • The predictive model accurately estimated localized muscle fatigue values with satisfactory R² and RMSE.
  • The MLR model outperformed the MLP model in predictive accuracy.

Conclusions:

  • A novel predictive model for localized muscle fatigue in MIS was successfully developed and validated.
  • The model leverages wearable technology and artificial intelligence (MLR and MLP).
  • Findings suggest potential for improved surgeon well-being and performance in MIS.