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Tremor estimation and filtering in robotic-assisted surgery.

Boqiang Jia1,2, Wenjie Wang1,2, Xin Tian1,2

  • 1Xian Polytechnic University Branch of Shaanxi Artificial Intelligence Joint Laboratory, Xi'an Polytechnic University, Shaangu Avenue, Xi'an, 710600 Shaanxi China.

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|December 9, 2025
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Summary

This study introduces a deep learning method to predict and suppress surgical hand tremors by integrating long-term and short-term signal features. The novel approach significantly reduces tremor estimation errors, enhancing surgical robot accuracy.

Keywords:
Motion predictionComputer assisted surgeryDeep learningTremor filtration

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Spontaneous hand tremors in surgeons can compromise the accuracy of surgical robots.
  • Accurate measurement and modeling of tremor signals are crucial for effective suppression.

Purpose of the Study:

  • To propose a deep learning-based prediction method for surgical hand tremor suppression.
  • To integrate long-term and short-term tremor signal features for improved estimation.

Main Methods:

  • Utilized a bidirectional Long-short-term memory network for long-term feature extraction.
  • Employed a Temporal Convolutional Network for short-term feature extraction.
  • Integrated features, optimized time step-size with a genetic algorithm, and used an end data compensation strategy.

Main Results:

  • The proposed deep learning model demonstrated superior performance compared to existing methods.
  • Significantly reduced tremor signal estimation error.
  • Suture experiments in a virtual surgical environment validated the method's effectiveness.

Conclusions:

  • The integrated deep learning approach effectively estimates and compensates for surgical hand tremors.
  • The method enhances surgical robot accuracy by suppressing hand tremors.
  • This provides a promising solution for improving precision in robotic surgery.