Key result
Classification of feature sets generated from foot GSR measurements using the LLC algorithm achieved the highest accuracy of 66.67% for drivers' stress level detection.
A machine learning approach using foot GSR measurements and the LLC algorithm achieved 66.67% accuracy in detecting driver stress levels.
May aid wearable driver monitoring development; leaves open validation in larger real-world cohorts.
In this study, the individual performances of the selected features, obtained from the ECG, GSR, EMG and RESP measurements by applying Pearson correlation analysis on the features accepted in the literature, were examined for the stress level detection. Accordingly, 2-, 1- and 3-dimensional feature sets were generated from ECG, Foot GSR and RESP measurements, respectively. These feature sets are classified by LLC, k-NN (k = 5), RF, DT and SVM algorithms. The feature set generated from the foot GSR measurement shows the best success with an accuracy of 66.67% when the LLC algorithm is used. This result indicates that the selected features are descriptive for stress level when they are used together.
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Yaman et al. (2019) studied Drivers' Stress. Bio-signal feature sets (ECG, GSR, EMG, RESP) and classification algorithms was evaluated on Stress level detection accuracy. Classification of feature sets generated from foot GSR measurements using the LLC algorithm achieved the highest accuracy of 66.67% for drivers' stress level detection.
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