Key result
The proposed stress-level-specific feature ensemble achieved up to 100% stress level recognition accuracy, providing an average increment of 24.85% compared to the full feature set.
The proposed feature set significantly improves stress level recognition accuracy and reduces false positive rates, suggesting potential utility in driving assistance systems.
May enhance stress detection models; leaves open validation for cardiovascular monitoring applications.
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.
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İdil Işıklı Esener (2019) studied Driver stress (n=10). Stress-level-specific feature ensemble vs. Full feature set (86-dimensional) was evaluated on Stress level recognition accuracy. The proposed stress-level-specific feature ensemble achieved up to 100% stress level recognition accuracy, providing an average increment of 24.85% compared to the full feature set.
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