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EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism
7 references·0 citing·26 second hop
EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism (2023)EEG and ECG-Based Multi-Sensor Fusion Com…Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences (2022)Research on a Real-Time Drive…Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks (2022)Multi-Level Classification of…Detecting driver fatigue using heart rate variability: A systematic review (2022)Detecting driver fatigue usin…A Systematic Survey of Driving Fatigue Monitoring (2022)A Systematic Survey of Drivin…Real time detection of driver fatigue based on CNN‐LSTM (2021)Real time detection of driver…Fatigue driving detection based on electrooculography: a review (2021)Fatigue driving detection bas…Driver fatigue transition prediction in highly automated driving using physiological features (2020)Driver fatigue transition pre…A critical review of the psychophysiology of driver fatigue (2001)Driver Inattention Monitoring System for Intelligent Vehicles: A Review (2011)Detecting Driver Drowsiness Based on Sensors: A Review (2012)Psychophysiological responses to takeover requests in conditionally automated driving (2020)Combat COVID-19 infodemic using explainable natural language processing models (2021)Human-Machine Shared Driving: Challenges and Future Directions (2022)Using EEG spectral components to assess algorithms for detecting fatigue (2008)Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness (2012)A CNN-Based Wearable System for Driver Drowsiness Detection (2023)A Systematic Review on Driver Drowsiness Detection Using Eye Activity Measures (2024)Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance (2002)Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue (2004)Real-Time System for Monitoring Driver Vigilance (2006)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)The link between fatigue and safety (2010)Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research (1988)Subjective and Objective Sleepiness in the Active Individual (1990)Development of a fatigue scale (1993)Driver Fatigue Detection Using Measures of Heart Rate Variability and Electrodermal Activity (2023)Fatigue State Recognition System for Miners Based on a Multimodal Feature Extraction and Fusion Framework (2024)Quantification of Sleepiness: A New Approach (1973)Drowsiness Detection in Drivers: A Systematic Review of Deep Learning-Based Models (2025)Validation of the Karolinska sleepiness scale against performance and EEG variables (2006)Rayyan—a web and mobile app for systematic reviews (2016)Air traffic controllers' mental fatigue recognition: A multi-sensor information fusion-based deep learning approach (2023)Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review (2024)
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References

  • 2022Detecting driver fatigue using heart rate variability: A systematic review166 cited
  • 2020Driver fatigue transition prediction in highly automated driving using physiological features117 cited
  • 2022A Systematic Survey of Driving Fatigue Monitoring81 cited
  • 2022Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences78 cited
  • 2021Real time detection of driver fatigue based on CNN‐LSTM62 cited
  • 2021Fatigue driving detection based on electrooculography: a review46 cited
  • 2022Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks40 cited

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