Driving Stress Estimation in Physiological Signals Based on Hierarchical Clustering and Multi-View Intact Space Learning

Detecting driver’s statuses is favorable for reducing the incidence of traffic accidents and ensuring driving security. This paper aims to develop an efficient system for driving stress detection under real driving circumstances. Multiple physiological signals, i.e., electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were collected and multi-modal features were extracted from time, spectral, and wavelet domains. The proposed approaches are motivated by three points: 1) Obvious individual difference affects the transferability of trained models to a new drive. Then, through dissimilarity evaluation and hierarchical clustering, we searched for subgroups of drives that presented relatively consistent feature distributions. Performing cross-drive modeling within each subgroup enables us to identify driver statuses more precisely with less computation cost; 2) fusing the high-dimensional physiological data from multiple views is beneficial to achieve a reliable assessment but brings new challenges for existing techniques. We adopted Multi-view Intact Space Learning (MISL) to integrate rich information from multiple perspectives by constructing a latent intact representation of the data; 3) most of the existing systems are offline. The current study made both offline and online analysis to validate the effectiveness of this research. Experimental results reveal that the proposed approaches can achieve competitive performance to state-of-the-art methods and can be developed into intelligent in-vehicle systems to detect driver’s unfavorable statuses, better adjust their negative affection, and avoid traffic accidents.