![]() ![]() In a practical application, utilizing fewer sensors for drivers during driving is a significant improvement due to the embarrassment which might occur during driving task. One of the remarkable points that makes GSR signals a better indicator than ECG signals is that GSR signal could be obtained by two sensors on the hand and foot skin, while the collection of ECG signals requires an additional sensor for the chest. GSR and ECG signals are the two most reliable physiological signals for stress recognition. According to this paper, the most informative signals belong to the cardiac signals. The vector Quantization learning method is used in to distinguish stress from relaxation. Here, parameter-based and trend-based techniques are proposed to generate features from ECG signals. Recently, A k-nearest-neighbor classifier learner is used in for the stress recognition purpose while driving. In addition, they used three types of classification methods such as SVM, Naive Bayes (NB) classifier, and decision tree. Zhai and Angus monitored and recorded three types of physiological signals, namely skin temperature (ST), GSR, and blood volume pulse (BVP), and introduced a novel automated system for stress classification. They used the support vector machine (SVM) technique based on electroencephalography (EEG) and ECG to recognize driver’s fatigue. However, this method is not suitable for classifying stress into three levels and particularly with considering a single signal. This research aimed to identify the stress level using the signal fusion of multiple sensors. Healey and Picard achieved an accuracy of 97.4% for two levels of high and moderate stress based on extracted data from EMG, RR, ECG, and GSR. introduced a method of stress estimation for drivers based on a dynamic Bayesian network (BN). Many studies have been conducted to computationally recognize and classify stress levels effectively. in Using quantitative analysis and different stress levels are classified based on ECG and GSR signals. An experimental procedure to elicit stress conditions has been designed and proposed by Martinez et al. The physiological signals including GSR, electrocardiogram (ECG), respiratory rate (RR), and electromyography (EMG) could be acquired for the aim of stress level monitoring. Employing physical indicators and analyzing physiological representatives are techniques that could be used to detect and classify stress. The physiological response of the human body to stress causes an increase in heart rate, respiration rate, muscle contraction, sweating, etc. Driving in stressful conditions such as city or freeway is associated with a higher rate of accidents, life-threatening situations, and compromises decision-making skills. Traffic congestion could be directly correlated to drivers’ mental health, hence developing a continuous monitoring system to automatically detect drivers’ stress is vital to enhance safety. In this study, the real data collected by Picard and his co-workers are used, available in the PHYSIONET database.Ĭar-induced accidents are a consequence of drivers’ stress or lack of attention which could be affected by emotional events. Besides, reducing the number of sensors during the measurement procedure would increase drivers’ safety by reducing the interference between human and measurement devices. Accordingly, this methodology can substantially reduce the necessity of resorting to the high number of sensors and the corresponding computational burden associated with signal analysis. The result indicates that the foot amplitude feature of the GSR signal solely is a reliable source of stress classification with an accuracy rate of 95.83% by applying the ANOVA approach. These two features are extracted from foot and hand GSR signals in three different scenarios for the sake of training. ![]() Three levels of stress are taken into account and two independent features including rising time and amplitude are extracted. In this study, we evaluate the feasibility and effectiveness of the analysis of variance (ANOVA) classifier learner on the single Galvanic Skin Response (GSR) signal. To obtain a high accuracy approach, a proper classification method should be applied to the most relevant physiological signal. Exploring the most reliable analysis method on a comprehensive physiological signal for stress realization has been commonly investigated in various studies. This paper presents a novel method of stress level classification using physiological signals during the real-world driving task. Although many studies have applied various methods in feature selection and classification, a desirable performance has not yet been achieved. Conventionally, multiple physiological signals are used in the field of stress realization. ![]()
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