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Mixture of experts extra tree-based sEMG hand gesture recognition.

Naveen Gehlot1,2, Ashutosh Jena3, Rajesh Kumar3,4

  • 1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. naveen.gehlot@manipal.edu.

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|March 2, 2026
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Summary

A novel Mixture of Experts Extra Tree classifier improves human hand gesture recognition using surface electromyography. This approach effectively handles overfitting and biases, enhancing robotic hand control accuracy.

Keywords:
Artificial intelligenceElectromyographyExtra treeHand gesture recognitionMachine learning classifierMixture of experts

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Area of Science:

  • Robotics and Machine Learning
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Surface electromyography (sEMG) is a non-invasive technique for human hand gesture recognition, crucial for robotic hand control.
  • Overfitting and biases are significant challenges in developing generalized classifiers for multi-gesture recognition using sEMG data.
  • Existing methods often struggle to achieve high accuracy and robustness across diverse gestures and subjects.

Purpose of the Study:

  • To introduce and evaluate a Mixture of Experts Extra Tree (MEET) classifier for enhanced human hand gesture recognition using sEMG.
  • To address the common issues of overfitting and bias in multi-gesture classification.
  • To compare the performance of the MEET classifier against eleven other models.

Main Methods:

  • Development of a Mixture of Experts Extra Tree classifier, utilizing individual Extra Tree models as experts and a gating Extra Tree model.
  • Collection of sEMG data from four subjects performing six distinct hand gestures.
  • Evaluation of the MEET classifier on both the collected dataset and a publicly available dataset with fifteen gesture classes.

Main Results:

  • The MEET classifier demonstrated superior performance compared to eleven other evaluated algorithms.
  • Accuracies for the MEET classifier on the collected data were 86.8%, 89.2%, 87.9%, and 78.4% across the four subjects.
  • The MEET classifier achieved an improved mean accuracy of 1.25% on the public dataset.

Conclusions:

  • The Mixture of Experts Extra Tree classifier is highly effective for accurate human hand gesture recognition from sEMG signals.
  • This model offers a robust solution for overcoming overfitting and bias in sEMG-based gesture classification.
  • The MEET classifier shows significant potential for advancing robotic hand control and human-computer interaction systems.