NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition
Driver distraction remains a critical factor in road accidents, necessitating intelligent systems for real-time detection. This study introduces a novel fNIRS-based method to to classify varying levels of driver distraction across diverse simulated scenarios, including cognitive, visual–manual, and...
Saved in:
| Main Authors: | Ghazal Bargshady, Hakki Gokalp Ustun, Yasaman Baradaran, Houshyar Asadi, Ravinesh C Deo, Jeroen Van Boxtel, Raul Fernandez Rojas |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/10/2965 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Measuring Brain Haemodynamic Activity and Afferent Visual Function: A Preliminary Study on the Relationship Between fNIRS, the King–Devick Test and Suspected Sport-Related Concussions
by: Mark Hecimovich, et al.
Published: (2025-01-01) -
Visualization and workload with implicit fNIRS-based BCI: toward a real-time memory prosthesis with fNIRS
by: Matthew Russell, et al.
Published: (2025-05-01) -
Verbal fluency tasks and attention problems in children with ADHD: evidence from fNIRS
by: Zouji Bian, et al.
Published: (2025-07-01) -
Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis
by: Mehshan Ahmed Khan, et al.
Published: (2025-02-01) -
Block-Wise Domain Adaptation for Workload Prediction from fNIRS Data
by: Jiyang Wang, et al.
Published: (2025-06-01)