Kundinger et al., 2020 - Google Patents
Assessment of the potential of wrist-worn wearable sensors for driver drowsiness detectionKundinger et al., 2020
View HTML- Document ID
- 2805228132340539431
- Author
- Kundinger T
- Sofra N
- Riener A
- Publication year
- Publication venue
- Sensors
External Links
Snippet
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially …
- 206010041349 Somnolence 0 title abstract description 138
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
-
- 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
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
-
- 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
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
-
- 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
-
- 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
- A61B5/48—Other medical applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kundinger et al. | Assessment of the potential of wrist-worn wearable sensors for driver drowsiness detection | |
Dalmeida et al. | HRV features as viable physiological markers for stress detection using wearable devices | |
Awais et al. | A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability | |
Gwak et al. | An investigation of early detection of driver drowsiness using ensemble machine learning based on hybrid sensing | |
Schmidt et al. | Wearable-based affect recognition—A review | |
Wang et al. | Real-time ECG-based detection of fatigue driving using sample entropy | |
Hussain et al. | Driving-induced neurological biomarkers in an advanced driver-assistance system | |
Shu et al. | A review of emotion recognition using physiological signals | |
Lee et al. | Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals | |
Ogino et al. | Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram | |
Halin et al. | Survey and synthesis of state of the art in driver monitoring | |
Li et al. | Detection of driver drowsiness using wavelet analysis of heart rate variability and a support vector machine classifier | |
Can et al. | How laboratory experiments can be exploited for monitoring stress in the wild: A bridge between laboratory and daily life | |
Daza et al. | Fusion of optimized indicators from Advanced Driver Assistance Systems (ADAS) for driver drowsiness detection | |
Magaña et al. | The effects of the driver’s mental state and passenger compartment conditions on driving performance and driving stress | |
Suzuki et al. | Constructing an emotion estimation model based on EEG/HRV indexes using feature extraction and feature selection algorithms | |
Siddiqui et al. | Respiration based non-invasive approach for emotion recognition using impulse radio ultra wide band radar and machine learning | |
Choi et al. | A biological signal-based stress monitoring framework for children using wearable devices | |
Hussain et al. | An explainable EEG-based human activity recognition model using machine-learning approach and LIME | |
Tervonen et al. | Ultra-short window length and feature importance analysis for cognitive load detection from wearable sensors | |
Younis et al. | Evaluating ensemble learning methods for multi-modal emotion recognition using sensor data fusion | |
Arefnezhad et al. | Driver monitoring of automated vehicles by classification of driver drowsiness using a deep convolutional neural network trained by scalograms of ECG signals | |
Ebrahimian et al. | Multi-level classification of driver drowsiness by simultaneous analysis of ECG and respiration signals using deep neural networks | |
Choi et al. | Driver identification system using normalized electrocardiogram based on adaptive threshold filter for intelligent vehicles | |
Rahman et al. | Vision-based driver’s cognitive load classification considering eye movement using machine learning and deep learning |