Abstract
When designing a car, the vehicle dynamics and handling are important aspects, as they can satisfy a purpose in professional racing, as well as contributing to driving pleasure and safety, real and perceived, in regular drivers. In this paper, we focus on the assessment of the emotional response in drivers while they are driving on a track with different car handling setups. The experiments were performed using a dynamic professional simulator prearranged with different car setups. We recorded various physiological signals, allowing us to analyze the response of the drivers and analyze which car setup is more influential in terms of stress arising in the subjects. We logged two skin potential responses (SPRs), the electrocardiogram (ECG) signal, and eye tracking information. In the experiments, three car setups were used (neutral, understeering, and oversteering). To evaluate how these affect the drivers, we analyzed their physiological signals using two statistical tests (t-test and Wilcoxon test) and various machine learning (ML) algorithms. The results of the Wilcoxon test show that SPR signals provide higher statistical significance when evaluating stress among different drivers, compared to the ECG and eye tracking signals. As for the ML classifiers, we count the number of positive or “stress” labels of 15 s SPR time intervals for each subject and each particular car setup. With the support vector machine classifier, the mean value of the number of positive labels for the four subjects is equal to 13.13% for the base setup, 44.16% for the oversteering setup, and 39.60% for the understeering setup. In the end, our findings show that the base car setup appears to be the least stressful, and that our system enables us to effectively recognize stress while the subjects are driving in the different car configurations.
Highlights
IntroductionWell-being assessment and quantification in domestic or in more general scenarios are research areas which are receiving increasing attention [1]
Introduction published maps and institutional affilWell-being assessment and quantification in domestic or in more general scenarios are research areas which are receiving increasing attention [1]
In addition to the previous analysis based on heart rate (HR) values, we evaluated several parameters of HRV, such as the mean value of normal-to-normal RR intervals, the standard deviation of RR intervals (SDNN), the root mean square of subsequent RR interval differences (RMSSD), the number of subsequent RR intervals differing more than 50 ms (NN50), the corresponding relative value in percentage (PNN50), the low frequency (LF) and high frequency (HF) power spectra, and the ratio LF/HF to be used as input features to the machine learning (ML)
Summary
Well-being assessment and quantification in domestic or in more general scenarios are research areas which are receiving increasing attention [1]. Well-being is of paramount importance, as anxiety and stress, for instance, are linked to bad driving behavior [2]. Other factors, such as drowsiness, are linked to car accidents [3]. The effects of frequent stressing trips can cause long-term health problems and increase the risk of cardiovascular [4] and locomotor [5] diseases. Physiological signals can be a source of information about stress in this scenario [6–8] and are widely employed in drivers’ mental state research (see [9])
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