A Novel Stress-Level-Specific Feature Ensemble for Drivers’ Stress Level Recognition

Yıl 2019, , 12 - 23, 28.06.2019

İdil Işıklı Esener

https://doi.org/10.35193/bseufbd.554791

Öz

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.

Anahtar Kelimeler

Stress recognition, Feature selection, Feature correlation

Sürücü Stres Seviyesi Tanıma için Stres-Seviyesine-Özgü Yeni Bir Öznitelik Topluluğu

Öz

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.

Anahtar Kelimeler

Stress Recognition, Feature Selection, Feature Correlation

Kaynakça

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