Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning

Abstract

Affective states have a critical role in driving performance and safety. They can degrade driver situation awareness and negatively impact cognitive processes, severely diminishing road safety. Therefore, detecting and assessing drivers' affective states is crucial in order to help improve the driving experience, and increase safety, comfort and well-being. Recent advances in affective computing have enabled the detection of such states. This may lead to empathic automotive user interfaces that account for the driver's emotional state and influence the driver in order to improve safety. In this work, we propose a multiview multi-task machine learning method for the detection of driver's affective states using physiological signals. The proposed approach is able to account for inter-drive variability in physiological responses while enabling interpretability of the learned models, a factor that is especially important in systems deployed in the real world. We evaluate the models on three different datasets containing real-world driving experiences. Our results indicate that accounting for drive-specific differences significantly improves model performance.

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

“…As drivers’ conditions should be detected as quickly as possible to prevent potential accidents, long signals (over 100 s) are not very helpful in detecting drivers’ stress early in actual situations. Lopez-Martinez et al [ 41 ] classified two stress levels using support vector machine (SVM) and achieved high accuracy. However, the achieved performance is 2.67% lower than that of our model based on the same length (30 s) of signals.…”

Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition

“…In a previous study, Wang and Guo employed the supervised ensemble classifier in conjunction with an unsupervised learning classifier to detect stress in drivers' foot galvanic skin response (GSR) data. Their suggested model detected stress with an accuracy of 90.1% [25]. Moreover, the combination of autoencoders and unsupervised deep learning to categorize mental stress related to HRV is a new approach that is expected to gain traction in 2019.…”

ECG-Based Driver’s Stress Detection Using Deep Transfer Learning and Fuzzy Logic Approaches

“…Several approaches in driver's stress detection for the real world and simulated driving conditions exist in the literature. Healey and Picard [16], Zhang et al [20], Chen et al [4], Haouij et al [21], Lopez-Martinez et al [22], Vargas-Lopez et al [23], and Dalmeida and Masala [24] proposed real-world driver's stress detection models using either single or fusion of different physiological signals including ECG, Heart Rate (HR), electromyogram (EMG), GSR, and RESP signals acquired from the PhysioNet realworld driving database [19]. Similarly, Lee et al [17], Bianco et al [25], and Zontone et al [26] have proposed driver's stress detection models during simulated driving conditions.…”

“…Previous work in this area is greatly based on traditional machine learning algorithms to classify driver's stress levels. Healey and Picard [16], Zhang et al [20], Chen et al [4], Haouij et al [21], Lopez-Martinez et al [22], Vargas-Lopez et al [23], Dalmeida and Masala [24], Lee et al [17], Bianco et al [25], and Zontone et al [26] have proposed driver's stress detection models in real world and simulated driving scenarios. However, extracting the best handcrafted features from different physiological and physical data used these models is always a challenging task.…”