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

Zebin JIANG, Ke ZHANG, Kuijun WU, Jie XU, Xinyan LI, Yu SUN, Xianliang GE, Ming MAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publicationIEEE 6th Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1560-1565
Number of pages6
ISBN (Electronic)9781665458641
DOIs
Publication statusPublished - 2022
Externally publishedYes

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

Fingerprint

Dive into the research topics of 'Mental workload recognition using ECG and machine learning in simulated flight tasks'. Together they form a unique fingerprint.

Cite this