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. 2022 Feb 16;12:2650. doi: 10.1038/s41598-022-05810-x

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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The PERCLOS residual error of the LS method to identify the parameters of the state transition equation. The root mean squares error (RMSE) between actual PERCLOS and modeled PERCLOS by state transition equation is 0.061. This error is higher in some parts of the data which are corresponding to either extremely low (alert) or extremely high PERCLOS (very drowsy) which are not of interest of this method since we want to track the transition of driver’s vigilance from alertness to drowsiness.