ArticleSensors (Basel, Switzerland)2023
EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 28 citations in OpenAlex.
- Application of Filter Bank to Improve Fatigue Monitoring in Wearable EEG-Based Brain-Computer Interface.NeuroSci · 2026Article
- A fatigue driving detection method based on driver posture and facial state analysis.Scientific reports · 2026Article
- Improving cognitive stress classification via multimodal EEG and ECG fusion: gender differences in physiological response.Scientific reports · 2026Article
- Multimodal sensor-based response mechanisms of drivers under adverse weather conditions using EEG spectral entropy and prediction entropy.Frontiers in neuroscience · 2026Article
- Research on the Identification of Road Hypnosis Based on the Fusion Calculation of Dynamic Human-Vehicle Data.Sensors (Basel, Switzerland) · 2025Article
- MCT-CNN-LSTM: A Driver Behavior Wireless Perception Method Based on an Improved Multi-Scale Domain-Adversarial Neural Network.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques.Sensors (Basel, Switzerland) · 2025Article
- Field-Programmable Gate Array (FPGA)-Based Lock-In Amplifier System with Signal Enhancement: A Comprehensive Review on the Design for Advanced Measurement Applications.Sensors (Basel, Switzerland) · 2025Review
- Wearable sensors-based assistive technologies for patient health monitoring.Frontiers in bioengineering and biotechnology · 2025Article
- Heart Diseases Recognition Model Based on HRV Feature Extraction over 12-Lead ECG Signals.Sensors (Basel, Switzerland) · 2024Article
- Advancements in Sensors and Analyses for Emotion Sensing.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 4 countries.
Funding
Abstract
A variety of technologies that could enhance driving safety are being actively explored, with the aim of reducing traffic accidents by accurately recognizing the driver's state. In this field, three mainstream detection methods have been widely applied, namely visual monitoring, physiological indicator monitoring and vehicle behavior analysis. In order to achieve more accurate driver state recognition, we adopted a multi-sensor fusion approach. We monitored driver physiological signals, electroencephalogram (EEG) signals and electrocardiogram (ECG) signals to determine fatigue state, while an in-vehicle camera observed driver behavior and provided more information for driver state assessment. In addition, an outside camera was used to monitor vehicle position to determine whether there were any driving deviations due to distraction or fatigue. After a series of experimental validations, our research results showed that our multi-sensor approach exhibited good performance for driver state recognition. This study could provide a solid foundation and development direction for future in-depth driver state recognition research, which is expected to further improve road safety.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.