Driver Inattention Monitoring System for Intelligent Vehicles: A Review

Dong, Yaomin; Hu, Zhencheng; Uchimura, Keiichi; Murayama, Nobuki

doi:10.1109/tits.2010.2092770

Cited by 554 publications

(144 citation statements)

References 62 publications

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“…In this regard, different physiological signals have been used to monitor alertness, such as spectral analysis of HRV (90% accuracy) [85] and EEG (0.913 ± 0.027 correlation between actual and estimated alertness levels) [86], wavelet coefficients of EEG combined with features from EOG and EMG (97-98% 3-class overall accuracy) [87], and time, spectral, and wavelet features from single-lead EEG [88]. Neuromuscular (EEG, EOG, EMG) and cardiac (ECG) signals have been analyzed predominantly in order to detect drowsiness, though additional physiological recordings (oximetry, skin conductance), physical measures (eye movement/blinks, face and mouth images), and driver's performance measures (steering wheel movements) have been also proposed as inputs to different pattern recognition methods, specially Bayesian networks, SVMs, and ensembles of linear classifiers [89][90][91]. The main limitation of these automated algorithms is that a great amount of data is needed to perform an accurate training of the pattern recognition method.…”

Section: Driver's Drowsiness Detectionmentioning

confidence: 99%

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Álvarez¹,

Cerezo-Hernández²,

Lopez-Muniz³

et al. 2017

Sleep Apnea - Recent Updates

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Sleep apnea-hypopnea syndrome (SAHS) is a chronic and highly prevalent disease considered a major health problem in industrialized countries. The gold standard diagnostic methodology is in-laboratory nocturnal polysomnography (PSG), which is complex, costly, and time consuming. In order to overcome these limitations, novel and simplified diagnostic alternatives are demanded. Sleep scientists carried out an exhaustive research during the last decades focused on the design of automated expert systems derived from artificial intelligence able to help sleep specialists in their daily practice. Among automated pattern recognition techniques, artificial neural networks (ANNs) have demonstrated to be efficient and accurate algorithms in order to implement computer-aided diagnosis systems aimed at assisting physicians in the management of SAHS. In this regard, several applications of ANNs have been developed, such as classification of patients suspected of suffering from SAHS, apnea-hypopnea index (AHI) prediction, detection and quantification of respiratory events, apneic events classification, automated sleep staging and arousal detection, alertness monitoring systems, and airflow pressure optimization in positive airway pressure (PAP) devices to fit patients' needs. In the present research, current applications of ANNs in the framework of SAHS management are thoroughly reviewed.

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Section: Driver's Drowsiness Detectionmentioning

confidence: 99%

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Álvarez¹,

Cerezo-Hernández²,

Lopez-Muniz³

et al. 2017

Sleep Apnea - Recent Updates

View full text Add to dashboard Cite

show abstract

“…However, this method has low accuracy and very susceptible to environmental noise. A facial expression such as eye closure and yawning mouth, as well as a head movement, are variables commonly analyzed in video-based data [17]. As drowsiness is also accompanied by distinctive body movements, force plate data that measure body sway may be very helpful to improve the accuracy of video-based data.…”

Section: Motion Analysis In the Driving Behavior Studymentioning

confidence: 99%

“…Pressure sensor and EMG is very common used to measure task performance, therefore, they should be more useful to detect task-related fatigue. Furthermore, as each measurement has its own deficiencies apart from their advantages, the combination of various measurement methods has been suggested to be used to improve the accuracy of detection [17].…”

Section: Motion Analysis In the Driving Behavior Studymentioning

confidence: 99%

Conceptual framework on the application of biomechanical measurement methods in driving behavior study

Sanjaya¹,

Sya'Bana²

2017

AIP Conference Proceedings

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“…The main methods of fatigue driving detection can be divided into subjective detection methods and objective detection methods [7][8]. The subjective detection methods require the driver to determine their degree of fatigue by checking the driving record sheet and their own physiological response.…”

Section: Introductionmentioning

confidence: 99%

A Review on Fatigue Driving Detection

Shi

Wang

2017

ITM Web Conf.

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Abstract:The socialization of automobile development has brought great convenience to people's travel. However, the rapid increase in the number of vehicles has also caused a series of problems. The increase in traffic accidents has brought great social casualties and economic losses. Fatigue driving, which is an important factor in the traffic accident, has aroused people's attention. This paper reviews all kinds of fatigue driving detection methods at present; compares various fatigue driving detection methods in terms of accuracy, real-time and cost; analyses the advantages and disadvantages of various methods; introduces the application of fatigue detection system in automobile; summarizes the current deficiencies and future development trends in the field of fatigue driving detection. The future research of this field will be more to the data fusion, computer vision and deep learning.

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Driver Inattention Monitoring System for Intelligent Vehicles: A Review

Cited by 554 publications

References 62 publications

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

Conceptual framework on the application of biomechanical measurement methods in driving behavior study

A Review on Fatigue Driving Detection

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