Driver Assistance System for Stress Recognition by Handcrafted Feature Extraction and Convolutional Neural Network

Stress is one of the emotional states that cause disruption in decision-making, that's why stress while driving can be dangerous. Identification of stress in drivers by smart algorithms and timely warning can prevent traffic accidents. In the proposed method to provide a stress detection driver assistance system, the feature vector is used as convolutional neural network input, which makes the training of the network stable in repeated repetitions, improves the weight update, and increases the classification accuracy. The drivedb database contains biosignals such as the ECG of drivers in city driving and in our proposed method, the ECG of this database is used. By using the proposed method, three stress states were identified with 93.6% accuracy, which has increased classification accuracy compared to similar methods.