@article{ref1,
title="Driver's mental workload prediction model based on physiological indices",
journal="International journal of occupational safety and ergonomics",
year="2019",
author="Yan, Shengyuan and Tran, Cong Chi and Wei, Yingying and Habiyaremye, Jean Luc",
volume="25",
number="3",
pages="476-484",
abstract="Developing an early warning model to predict the driver's mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new driver's MWL and their work performance, regarding the number of errors. Additionally, the Group method of data handling (GMDH) is used to establish the driver's MWL predictive model based on subjective rating (NASA task load index (NASA-TLX)) and six physiological indices. The results indicate that NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an R(2) value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver's work performance.<p /> <p>Language: en</p>",
language="en",
issn="1080-3548",
doi="10.1080/10803548.2017.1368951",
url="http://dx.doi.org/10.1080/10803548.2017.1368951"
}