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A parallel classification strategy to simultaneous control elbow, wrist, and hand movements.

Francesca Leone1, Cosimo Gentile2, Francesca Cordella2

  • 1Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, Rome, Italy. f.leone@unicampus.it.

Journal of Neuroengineering and Rehabilitation
|January 29, 2022
PubMed
Summary

This study introduces a novel Logistic Regression (LR) strategy for myoelectric control, enabling simultaneous classification of 3 Degrees of Freedom (DoFs) motions. The new approach outperforms traditional Linear Discriminant Analysis (LDA) in real-time recognition accuracy for prosthetic control.

Keywords:
Multi-DoFs controlPattern recognitionProsthetic controlReal-time and offline performanceUpper limb

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Engineering
  • Signal Processing

Background:

  • Pattern recognition (PR) algorithms are crucial for myoelectric control systems, predicting complex electromyography (EMG) patterns.
  • Existing Linear Discriminant Analysis (LDA) classifiers struggle with simultaneous classification of multiple Degrees of Freedom (DoFs), limiting prosthetic control.
  • Current strategies lack online performance measures for real-time prosthetic applications.

Purpose of the Study:

  • To introduce a novel Logistic Regression (LR) based classification strategy for simultaneous 3 DoFs motion control.
  • To evaluate the performance of the LR strategy against LDA for myoelectric control.
  • To improve real-time recognition accuracy in prosthetic limb control systems.

Main Methods:

  • A parallel PR-based strategy using three joint classifiers (elbow, wrist, hand) was implemented.
  • Logistic Regression (LR) with a regularization parameter was employed for classification.
  • The strategy was tested on 15 healthy subjects using six surface EMG sensors, classifying 27 motion classes.

Main Results:

  • The LR classifier achieved statistically better real-time recognition results compared to the LDA classifier for all motion classes.
  • Classification error rate was maintained under 10% for discrete and complex elbow, hand, and wrist motions.
  • The study demonstrated effective simultaneous control of elbow, hand, and wrist joints with minimal EMG electrodes.

Conclusions:

  • A novel parallel PR-based strategy using LR shows promise for classifying up to 3 DoFs.
  • The proposed LR strategy offers superior real-time performance for myoelectric control compared to LDA.
  • This approach facilitates simultaneous control of multiple prosthetic joints with high accuracy.