Driver Behaviour Detection Using Multimodal Physiological Signals and Regularized Deep Kernel Learning
Files
Date
2025-01-07
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
3417
Ending Page
Alternative Title
Abstract
Integrating contextual cues from the driver, cabin, and surrounding environment is crucial for enhancing semi-automated vehicle safety. Current systems often rely on video streams to capture driver behaviours and environmental conditions, yet face challenges in low-light settings and privacy intrusion. In this study, we investigated multimodal physiological signals and Kernelized Convolutional Neural Networks (K-CNN) for assessing driver behaviour. For this, electrocardiography (ECG) and respiration (RSP) data were collected from 15 healthy volunteers wearing smart shirts in two driving scenarios. Signals were sampled at 256Hz and 128Hz, respectively. From this data we have extracted time-domain, frequency-domain and non-linear features using Neurokit2 toolkit. These features are fed to the K-CNN framework where task specific representation is extracted using Radial Basis Function, Linear, Polynomial Degree 2 and Polynomial Degree 3 kernels and these representation fed to 1D CNN to extract temporal features and these features are fused to detect driver behaviour using sigmoid. The proposed approach is able to classify driver behaviour states using multimodal data, achieving an average accuracy of 70.54% and an average F-score of 62.22% for linear kernel. Textile-based physiological data proved more reliable in classifying car driver behaviour states using K-CNN than with conventional machine learning techniques.
Description
Keywords
Healthcare in Motion: Innovations in Mobility-integrated Health Systems, driver behavior assessment, kernelized convolutional neural networks, multimodal physiological signals, textitle technology
Citation
Extent
10
Format
Geographic Location
Time Period
Related To
Proceedings of the 58th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.