Supervised Learning Techniques for Stress Detection in Car Drivers

Zontone, Pamela; Affanni, Antonio; Bernardini, R.; Linz, Leonida Del; Piras, Alessandro; Rinaldo, R.

doi:10.25046/aj050603

Cited by 14 publications

(11 citation statements)

References 42 publications

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“…All of these ML classifiers were implemented in Matlab, with the help of the Machine Learning Toolbox. A 10-fold cross validation step was also applied to all of these algorithms for hyperparameter optimization (see also [25]). The ML classifiers, after being trained on the described larger dataset, were used for the testing procedure on the dataset composed of the SPR and ECG acquired from the subjects while they were driving in a simulated urban area.…”

Section: Feature Extraction and ML Classificationmentioning

confidence: 99%

Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving

Zontone

Affanni

Piras

et al. 2022

Sensors

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In this paper, we propose a relatively noninvasive system that can automatically assess the impact of traffic conditions on drivers. We analyze the physiological signals recorded from a set of individuals while driving in a simulated urban scenario in two different traffic scenarios, i.e., with traffic and without traffic. The experiments were carried out in a laboratory located at the University of Udine, employing a driving simulator equipped with a moving platform. We acquired two Skin Potential Response (SPR) signals from the hands of the drivers, and an electrocardiogram (ECG) signal from their chest. In the proposed scheme, the SPR signals are then processed through a Motion Artifact (MA) removal algorithm such that possible motion artifacts arising during the drive are reduced. An analysis considering the scalogram of the single cleaned SPR signal is presented. This signal, along with the ECG, is then fed to various Machine Learning (ML) algorithms. More specifically, some statistical features are extracted from each signal segment which, after being analyzed through a binary ML model, are labeled as corresponding to a stressful situation or not. Our results confirm the applicability of the proposed approach to identify stress in the two scenarios. This is also in accordance with our findings considering the SPR signal scalograms.

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Section: Feature Extraction and ML Classificationmentioning

confidence: 99%

Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving

Zontone

Affanni

Piras

et al. 2022

Sensors

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“…We then computed the mean of the test results for all individuals, and we applied a relabel procedure to overcome the issue of single isolated "1" labels along the course [44]. All of these ML models were developed in Matlab (2017a), also using the Bayesian optimization during the training phase for hyperparameter optimization (see also [45]). The classifiers' hyperparameters are obtained by selecting the auto hyperparameter optimization of the Matlab Statistics and Machine Learning Toolbox [46].…”

Section: Spr Signal Analysismentioning

confidence: 99%

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

et al. 2022

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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.

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“…The Bayesian optimization was also used during the training procedure for all of the classifiers (for hyperparameter tuning). A Radial Basis Function (RBF) kernel was employed for the SVM model (see also [23]).…”

Section: Proposed Systemmentioning

confidence: 99%

“…In previous works, the authors carried out some experiments with the aid of a driving simulator platform, recreating a highway [22], [23] and inducing stress by adding obstacles along the course. We collected the ECG in addition to the Skin Potential Response (SPR) data, and we employed ML and DL algorithms to detect stress in the test individuals.…”

Section: Introductionmentioning

confidence: 99%

Skin potential response for stress recognition in simulated urban driving

et al. 2021

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In this paper, we address the problem of possible stress conditions arising in car drivers, thus affecting their driving performance. We apply various Machine Learning (ML) algorithms to analyse the stress of subjects while driving in an urban area in two different situations: one with cars, pedestrians and traffic along the course, and the other characterized by the complete absence of any of these possible stress-inducing factors. To evaluate the presence of a stress condition we use two Skin Potential Response (SPR) signals, recorded from each hand of the test subjects, and process them through a Motion Artifact (MA) removal algorithm which reduces the artifacts that might be introduced by the hand movements. We then compute some statistical features starting from the cleaned SPR signal. A binary classification ML algorithm is then fed with these features, giving as an output a label that indicates if a time interval belongs to a stress condition or not. Tests are carried out in a laboratory at the University of Udine, where a car driving simulator with a motorized motion platform has been prearranged. We show that the use of one single SPR signal, along with the application of ML algorithms, enables the detection of possible stress conditions while the subjects are driving, in the traffic and no traffic situations. As expected, we observe that the test individuals are less stressed in the situation without traffic, confirming the effectiveness of the proposed slightly invasive system for detection of stress in drivers.

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Supervised Learning Techniques for Stress Detection in Car Drivers

Cited by 14 publications

References 42 publications

Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving

Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving

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

Skin potential response for stress recognition in simulated urban driving

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