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

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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