Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability

Hanyu Wang, Dengkai Chen*, Yuexin Huang, Yahan Zhang, Yidan Qiao, Jianghao Xiao, Ning Xie, Hao Fan

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Abstract

This study aimed to enhance the real-time performance and accuracy of vigilance assessment by developing a hidden Markov model (HMM). Electrocardiogram (ECG) signals were collected and processed to remove noise and baseline drift. A group of 20 volunteers participated in the study. Their heart rate variability (HRV) was measured to train parameters of the modified hidden Markov model for a vigilance assessment. The data were collected to train the model using the Baum–Welch algorithm and to obtain the state transition probability matrix (Formula presented.) and the observation probability matrix (Formula presented.). Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and the Viterbi algorithm was used to find the optimal state, which was compared with the actual state. The constructed vigilance assessment model had a high accuracy rate, and the accuracy rate of data prediction for these three volunteers exceeded 80%. Our approach can be used in wearable products to improve their vigilance level assessment functionality or in other fields that have key positions with high concentration requirements and monotonous repetitive work.

Original languageEnglish
Article number638
Number of pages19
JournalBrain Sciences
Volume13
Issue number4
DOIs
Publication statusPublished - 2023

Keywords

  • heart rate variability
  • hidden Markov model
  • psychomotor vigilance task
  • vigilance
  • visual search task
  • wearable device

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