@article{article_554791, title={A Novel Stress-Level-Specific Feature Ensemble for Drivers’ Stress Level Recognition}, journal={Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi}, volume={6}, pages={12–23}, year={2019}, DOI={10.35193/bseufbd.554791}, author={Işıklı Esener, İdil}, keywords={Stress Recognition,Feature Selection,Feature Correlation}, abstract={<p> <span class="IEEEAbstractHeadingChar"> <span lang="en-us" style="font-size:9pt;font-family:’Times New Roman’;" xml:lang="en-us">This
paper proposes a novel feature set for drivers’ stress level recognition. The
proposed feature set consists of data-independent and almost uncorrelated feature
pairs for each stress level with very strong intra-class and relatively weak
inter-class correlations, constructed by realizing a correlation analysis on
the popular features studied in the literature. By using the proposed feature
set, a maximum of 100% stress level recognition accuracy is achieved with an
average increment of 24.85% while a mean reduction rate of 88.01% is satisfied
in false positive rate compared to the full feature set. These outcomes clearly
show that the proposed feature set can confidently be integrated into the
driving assistance systems. </span> </span> <br /> </p>}, number={1}, publisher={Bilecik Şeyh Edebali Üniversitesi}