[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Lei et al., 2018 - Google Patents

Study on driving fatigue evaluation system based on short time period ECG signal

Lei et al., 2018

View PDF
Document ID
2777803620235507026
Author
Lei J
Liu F
Han Q
Tang Y
Zeng L
Chen M
Ye L
Jin L
Publication year
Publication venue
2018 21st international conference on intelligent transportation systems (ITSC)

External Links

Snippet

Fatigue driving is one of the most important causes of traffic accidents. Real-time fatigue driving detection and early warning is very important to reduce the number of traffic accidents, injuries, and deaths. Although traditional ECG (Electrocardiography) based …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0476—Electroencephalography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0402—Electrocardiography, i.e. ECG
    • A61B5/0452—Detecting specific parameters of the electrocardiograph cycle
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state
    • A61B5/18—Devices for psychotechnics; Testing reaction times; Devices for evaluating the psychological state for vehicle drivers or machine operators
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/04012—Analysis of electro-cardiograms, electro-encephalograms, electro-myograms
    • A61B5/04017—Analysis of electro-cardiograms, electro-encephalograms, electro-myograms by using digital filtering
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/04—Detecting, measuring or recording bioelectric signals of the body of parts thereof
    • A61B5/0488—Electromyography

Similar Documents

Publication Publication Date Title
Gurudath et al. Drowsy driving detection by EEG analysis using wavelet transform and K-means clustering
Chai et al. Driver fatigue classification with independent component by entropy rate bound minimization analysis in an EEG-based system
Solovey et al. Classifying driver workload using physiological and driving performance data: two field studies
Patel et al. Applying neural network analysis on heart rate variability data to assess driver fatigue
Lei et al. Study on driving fatigue evaluation system based on short time period ECG signal
Gwak et al. Early detection of driver drowsiness utilizing machine learning based on physiological signals, behavioral measures, and driving performance
Siam et al. Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques
Lin et al. Heartbeat classification using normalized RR intervals and wavelet features
CN103919565A (en) Fatigue driving electroencephalogram signal feature extraction and identification method
CN104461007A (en) Driver-car interactive system assisting driver based on electroencephalograms
Wang et al. Modeling and recognition of driving fatigue state based on RR intervals of ECG data
Begum et al. Mental state monitoring system for the professional drivers based on Heart Rate Variability analysis and Case-Based Reasoning
Ke et al. Drowsiness detection system using heartbeat rate in android-based handheld devices
Hu et al. Analysis on biosignal characteristics to evaluate road rage of younger drivers: A driving simulator study
CN106781283A (en) A kind of method for detecting fatigue driving based on soft set
Wang et al. EEG-based mental fatigue assessment during driving by using sample entropy and rhythm energy
Chai et al. Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system
Sihem et al. A body area network for ubiquitous driver stress monitoring based on ECG signal
Singh et al. Physical and physiological drowsiness detection methods
Rezaee et al. EEG-based driving fatigue recognition using hybrid deep transfer learning approach
Akbar et al. Three drowsiness categories assessment by electroencephalogram in driving simulator environment
Nema et al. Wavelet-frequency analysis for the detection of discontinuities in switched system models of human balance
Ma et al. Vigilance estimation by using electrooculographic features
Dey et al. Automatic detection of drowsiness in EEG records based on time analysis
Zhu et al. Eeg-based system using deep learning and attention mechanism for driver drowsiness detection