Analysis of Physiological Signals for Stress Recognition with Different Car Handling Setups

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.

Drivers’ Mental Engagement Analysis Using Multi-Sensor Fusion Approaches Based on Deep Convolutional Neural Networks

“…In this study, we aimed to explore the benefits of sensor fusion by synchronizing multiple developed biosensors on the same BSN and implementing DL algorithms with multiple sensor fusion approaches. In previous works, we assessed the emotional states of drivers using machine learning approaches with either SPR signals only [54,55] or a combination of SPR and ECG signals [56][57][58]. In this work, we focused on evaluating the level of drivers' mental engagement during manual and autonomous driving scenarios by employing advanced DL models that integrate EEG, SPR, and ECG signals.…”

Towards non-invasive PTSD diagnosis: Utilising EEG based Emotion Recognition with the DEAP Database

“…A novel paradigm for collecting physiological data in multi-sensory emotion detection was proposed by Asiain et al [21]. In order to aid in the identi cation of stress, Zontone et al [22] created a technique to gauge drivers' emotional reactions in a variety of driving situations. In order to evaluate car interior acceleration noises in conjunction with physiological inputs and produce precise sound quality judgements, Xie et al [23] created a hybrid deep neural network.…”

Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

“…Asiain et al [21] proposed a novel platform of physiological signal acquisition for multi-sensory emotion detection to record and analyze different physiological signals, and the most important features of the proposed platform were compared with those of a proven wearable device. Zontone et al [22] built a system to assess the emotional response in drivers while they were driving on a track with different car handling setups. The experimental results based on the system indicated that the base car setup appeared to be the least stressful, and that the presented system enabled one to effectively recognize stress while the subjects were driving in the different car configurations.…”