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Assessing workload in using electromyography (EMG)-based prostheses.

Junho Park1, Joseph Berman2, Albert Dodson3,4

  • 1Wm Michael Barnes '64 Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, USA.

Ergonomics
|June 2, 2023
PubMed
Summary
This summary is machine-generated.

This study developed machine learning models to assess cognitive workload in electromyography (EMG)-based prosthetic devices. Naïve Bayes and Random Forest algorithms show promise in predicting workload for improved prosthetic design.

Keywords:
Mental workloadclassificationmachine learningprosthesis

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Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Rehabilitation Engineering

Background:

  • Prosthetic device use imposes a significant cognitive load on users.
  • Accurate assessment of cognitive workload is crucial for optimizing prosthetic design and usability.

Purpose of the Study:

  • To investigate and compare classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices.
  • To identify key input features that best predict cognitive workload.

Main Methods:

  • Utilized electromyography (EMG) signals, eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes as input features.
  • Applied feature selection algorithms, hyperparameter tuning (grid search), and k-fold cross-validation for model optimization.
  • Evaluated model performance using classification accuracy, AUC, precision, recall, and F1 scores.

Main Results:

  • Task performance measures, pupillometry data, and CPM outcomes were identified as highly informative features.
  • Naïve Bayes (NB) and Random Forest (RF) algorithms demonstrated the most promising performance in classifying cognitive workload.
  • The developed models achieved high accuracy with low computational cost.

Conclusions:

  • Machine learning models, particularly NB and RF, can effectively classify cognitive workload in EMG-based prosthetics.
  • These models can assist manufacturers and clinicians in predicting cognitive workload during early design phases.
  • The findings support the use of these algorithms for assessing prosthetic device usability.