Wang, H.; Chen, D.; Qiao, Y.; Xiao, J.; Zhang, Y.; Huang, Y.; Xie, N. An Approach for Vigilance Assessment using Hidden Markov Model. Preprints2022, 2022100161. https://doi.org/10.20944/preprints202210.0161.v1
APA Style
Wang, H., Chen, D., Qiao, Y., Xiao, J., Zhang, Y., Huang, Y., & Xie, N. (2022). An Approach for Vigilance Assessment using Hidden Markov Model. Preprints. https://doi.org/10.20944/preprints202210.0161.v1
Chicago/Turabian Style
Wang, H., Yuexin Huang and Ning Xie. 2022 "An Approach for Vigilance Assessment using Hidden Markov Model" Preprints. https://doi.org/10.20944/preprints202210.0161.v1
Abstract
Purpose: To construct a hidden Markov model (HMM) for vigilance assessment to improve the real-time performance and accuracy of current vigilance measurement. Methods: ECG signal was collected by sensors, while the noise and baseline drift was eliminated from the original ECG signal. 10 volunteers were randomly selected. Their heart rate variability (HRV) were measured and trained parameters of the modified Hidden Markov model for vigilance assessment. Then, these data were collected to optimize using the Baum-Welch algorithm and obtained the state transition probability matrix A ̂ and the observation probability matrix B ̂. Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and used the Viterbi algorithm to find the optimal state, which compared with the actual state. Results: The constructed vigilance assessment model had a high accuracy rate the accuracy rate of data prediction for these three volunteers exceeded 80%. Conclusion: The Hidden Markov model for vigilance assessment can accurately predict the vigilance level and indicate broad application prospects.
Copyright:
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