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Concurrent Model for Three Negative Emotions Using Heart Rate Variability in a Driving Simulator Environment

In this study, the feasibility of conducting a concurrent estimation of drowsiness, stress, and tiredness by heart rate variability (HRV) in a driving simulator environment was examined. Subjects were required to attend a 120-min driving session four times: two morning and two afternoon sessions. Blood pressure and salivary amylase were also recorded to assess acute stress. A set of estimators was prepared, and stepwise regression was conducted on two different models at p = 0.05. In this work, it was shown that the use of a stepwise method and additional estimator capable of extracting significant and relevant information for multiple emotions with average performance in the form of the correlation coefficient(root mean square error) can increase up to 0.68 ± 0.12 (0.66 ± 0.28), 0.72 ± 0.13 (0.43 ± 0.21), and 0.71 ± 0.13 (0.48 ± 0.21), corresponding to drowsiness, stress, and tiredness, respectively. The results suggest that a single time series of HRV can extract more than one emotion, thus enabling a concurrent model to be developed. It was also observed that physiological behavior while driving works in a more complex way. The current evidence indicates the feasibility of conducting concurrent emotion assessment during driving.