Mental workload recognition using ECG and machine learning in simulated flight tasks

Effective mental workload recognition is of great significance for improving task performance and reducing accidents. Although prior research has achieved approximately 95% accuracy using electroencephalography (EEG), it is difficult to transplant into actual task scenarios due to the low portabilit...

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Bibliographic Details
Published inIEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (Online) pp. 1560 - 1565
Main Authors Jiang, Zebin, Zhang, Ke, Wu, Kuijun, Xu, Jie, Li, Xinyan, Sun, Yu, Ge, Xianliang, Mao, Ming
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.10.2022
Subjects
Brain modeling
ECG
Electrocardiography
Electroencephalography
Feature extraction
flight simulation
HRV
Machine learning
mental workload
Performance evaluation
Radio frequency
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Summary:Effective mental workload recognition is of great significance for improving task performance and reducing accidents. Although prior research has achieved approximately 95% accuracy using electroencephalography (EEG), it is difficult to transplant into actual task scenarios due to the low portability of the device. Here, we introduce a mental workload recognition solution to give consideration to high recognition accuracy and portability. Heart rate variability (HRV) was extracted from the electrocardiogram (ECG) signals of 26 participants during simulated flight tasks, and the sensitive features were screened out using the generalized linear mixed model. Then, the three mental workload levels were classified and evaluated in combination with the machine learning method. Our solution achieved an accuracy of 98% for subject-independent mental workload recognition.
ISSN:2689-6621
DOI:10.1109/IAEAC54830.2022.9930029