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

ABSTRACT

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.Practitioner summary: This study investigated the use of machine learning algorithms for classifying the cognitive workload of prosthetic devices. The findings suggested that the models could predict workload with high accuracy and low computational cost and could be used in assessing the usability of prosthetic devices in the early phases of the design process.Abbreviations 3d 3 dimensional; ADL Activities for daily living; ANN Artificial neural network; AUC Area under the receiver operation characteristic curve; CC Continuous control; CPM Cognitive performance model; CPM-GOMS Cognitive-Perceptual-Motor GOMS; CRT Clothespin relocation test; CV Cross validation; CW Cognitive workload; DC Direct control; DOF Degrees of freedom; ECRL Extensor carpi radialis longus; ED Extensor digitorum; EEG Electroencephalogram; EMG Electromyography; FCR Flexor carpi radialis; FD Flexor digitorum; GOMS Goals, Operations, Methods, and Selection Rules; LDA Linear discriminant analysis; MAV Mean absolute value; MCP Metacarpophalangeal; ML Machine learning; NASA-TLX NASA task load index; NB Naïve Bayes; PCPS Percent change in pupil size; PPT Purdue Pegboard Test; PR Pattern recognition; PROS-TLX Prosthesis task load index; RF Random forest; RFE Recursive feature selection; SHAP Southampton hand assessment protocol; SFS Sequential feature selection; SVC Support vector classifier.