Self-Supervised Learning for Near-Wild Cognitive Workload Estimation.
Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-based models can produce feedback from physiological data such as electroencephalography (EEG) and electrocardiography (ECG). Supervised machine learning requires large training data sets that are (1) relevant and d...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 22 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
11/22/2024
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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=181090203&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181090203 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 11/22/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181090203 181090203 181090203 10.1007/s10916-024-02122-7 181090203 ppf: 1 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Self-Supervised Learning for Near-Wild Cognitive Workload Estimation. aug: au: Rafiei, Mohammad H. Gauthier, Lynne V. Adeli, Hojjat Takabi, Daniel affil: https://ror.org/00za53h95 Whiting School of Engineering, Johns Hopkins University, 21218, Baltimore, MD, USA sug: subj: Machine Learning Mental Fatigue Cognition Workload Measurement Human Feedback Decision Making, Clinical Health Care Errors Prevention and Control Electroencephalography Electrocardiography Monitoring, Physiologic Artifacts Descriptive Statistics ab: Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-based models can produce feedback from physiological data such as electroencephalography (EEG) and electrocardiography (ECG). Supervised machine learning requires large training data sets that are (1) relevant and decontaminated and (2) carefully labeled for accurate approximation, a costly and tedious procedure. Commercial over-the-counter devices are low-cost resolutions for the real-time collection of physiological modalities. However, they produce significant artifacts when employed outside of laboratory settings, compromising machine learning accuracies. Additionally, the physiological modalities that most successfully machine-approximate cognitive workload in everyday settings are unknown. To address these challenges, a first-ever hybrid implementation of feature selection and self-supervised machine learning techniques is introduced. This model is employed on data collected outside controlled laboratory settings to (1) identify relevant physiological modalities to machine approximate six levels of cognitive-physical workloads from a seven-modality repository and (2) postulate limited labeling experiments and machine approximate mental-physical workloads using self-supervised learning techniques. pubtype: Academic Journal doctype: equations & formulas research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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