Driver Behaviour Detection Using Multimodal Physiological Signals and Regularized Deep Kernel Learning

Simple item page
dc.contributor.authorKaveti, Pavan
dc.contributor.authorGanapathy, Nagarajan
dc.date.accessioned2024-12-26T21:07:13Z
dc.date.available2024-12-26T21:07:13Z
dc.date.issued2025-01-07
dc.description.abstractIntegrating 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.412
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other94bf5365-5c18-4188-a5d6-709ad52ed1c0
dc.identifier.urihttps://hdl.handle.net/10125/109255
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHealthcare in Motion: Innovations in Mobility-integrated Health Systems
dc.subjectdriver behavior assessment, kernelized convolutional neural networks, multimodal physiological signals, textitle technology
dc.titleDriver Behaviour Detection Using Multimodal Physiological Signals and Regularized Deep Kernel Learning
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage3417

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0335.pdf
Size:
3.64 MB
Format:
Adobe Portable Document Format
Download

Collections

Healthcare in Motion: Innovations in Mobility-integrated Health Systems