Abstract
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.
Original language | English |
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Title of host publication | IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1560-1565 |
Number of pages | 6 |
ISBN (Electronic) | 9781665458641 |
DOIs | |
Publication status | Published - 2022 |
Externally published | Yes |
Bibliographical note
XG and MM are co-corresponding authors. ZJ and KZ contributed equally to this work and should be considered co-first authors.Funding
This study was supported by the National Natural Science Foundation of China (Grant No. T2192931).
Keywords
- ECG
- flight simulation
- HRV
- mental workload