Key result
A driving stress detection system using hierarchical clustering and Multi-view Intact Space Learning achieved competitive performance to state-of-the-art methods in both offline and online analyses.
Why the study?
Detecting driver stress is favorable for reducing traffic accidents and ensuring driving security, but existing techniques face challenges with individual variability, fusing multi-view physiological data, and offline limitations.
A novel machine learning approach using physiological signals can effectively detect driving stress, potentially aiding in the development of intelligent in-vehicle systems to prevent traffic accidents.
Answers are grounded in this paper and cite their sources.
May enable in-vehicle stress alerts to reduce incident risk; leaves open prospective validation before clinical use.
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.
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Lanlan Chen (2021) studied Driving stress. Hierarchical clustering and Multi-view Intact Space Learning (MISL) vs. State-of-the-art methods was evaluated on Driving stress detection performance. A driving stress detection system using hierarchical clustering and Multi-view Intact Space Learning achieved competitive performance to state-of-the-art methods in both offline and online analyses.
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