Assessing workload in using electromyography (EMG)-based prostheses.
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...
| Publicado en: | Ergonomics Vol. 67; no. 2; pp. 257 - 274 |
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| Autores principales: | , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175980522&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175980522 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00140139 ERO jtl: Ergonomics issn: 00140139 maglogo: Y pubinfo: dt: Feb2024 vid: 67 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 175980522 164030739 175980522 175980522 10.1080/00140139.2023.2221413 175980522 ppf: 257 ppct: 17 formats: tig: atl: Assessing workload in using electromyography (EMG)-based prostheses. aug: au: Park, Junho Berman, Joseph Dodson, Albert Liu, Yunmei Armstrong, Matthew Huang, He Kaber, David Ruiz, Jaime Zahabi, Maryam affil: Wm Michael Barnes '64 Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, USA sug: subj: Prostheses and Implants Equipment and Supplies Cognition Evaluation Workload Electromyography Methods Machine Learning Utilization Algorithms Utilization Workload Measurement Eye Movements Human Male Female Adult Prediction Models Deep Learning Neural Networks (Computer) ROC Curve Random Forest Prosthesis Design Descriptive Statistics Funding Source Adult: 19-44 years Male Female ab: 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 pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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