Using prosthetic devices requires a substantial cognitive workload. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes. Features selection algorithm, hyperparameter tuning with grid search, and k-fold cross-validation were applied to select the most important features and find the optimal models. Classification accuracy, the area under the receiver operation characteristic curve (AUC), precision, recall, and F1 scores were calculated to compare the models’ performance. The findings suggested that task performance measures, pupillometry data, and CPM outcomes, combined with the naïve bayes (NB) and random forest (RF) algorithms, are most promising for classifying cognitive workload. The proposed algorithms can help manufacturers/clinicians predict the cognitive workload of future EMG-based prosthetic devices in early design phases.
  • Headshot of Jaime Ruiz wearing a HololensJaime Ruiz
  • As well as: Junho Park, Joseph Berman, Albert Dodson, Yunmei Liu, Matthew Armstrong, He Huang, David Kaber, and Maryam Zahabi

Junho Park, Joseph Berman, Albert Dodson, Yunmei Liu, Matthew Armstrong, He Huang, David Kaber, Jaime Ruiz & Maryam Zahabi. 2023. Assessing workload in using electromyography (EMG)-based prostheses, Ergonomics, DOI: 10.1080/00140139.2023.2221413

@article{doi:10.1080/00140139.2023.2221413,
author = {Junho Park and Joseph Berman and Albert Dodson and Yunmei Liu and Matthew Armstrong and He Huang and David Kaber and Jaime Ruiz and Maryam Zahabi},
title = {Assessing workload in using electromyography (EMG)-based prostheses},
journal = {Ergonomics},
volume = {0},
number = {0},
pages = {1-17},
year  = {2023},
publisher = {Taylor & Francis},
doi = {10.1080/00140139.2023.2221413}
}