scholarly journals Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qingjun Wang ◽  
Zhendong Mu
Keyword(s):  
Traffic Accidents ◽  
Information Source ◽  
Steering Wheel ◽  
Detection Accuracy ◽  
Fusion Method ◽  
Eyelid Closure ◽  
Signal Fusion ◽  
Fusion Methods ◽  
Driving Fatigue ◽  
Fatigue Detection

Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.

scholarly journals A two-step vibration-sound signal fusion method for weak fault feature detection in rolling bearing systems

2021 ◽  
Vol 13 (12) ◽  
pp. 168781402110671
Author(s):  
Guanchen Wu ◽  
Nengyu Yan ◽  
Kwang-nam Choi ◽  
Hoekyung Jung ◽  
Kerang Cao
Keyword(s):  
Feature Detection ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sound Signal ◽  
Detection Accuracy ◽  
Fusion Method ◽  
Signal Fusion ◽  
Signal Features ◽  
Weak Fault

The vibration and sound signals get widely applications in fault diagnosis of rolling bearing systems, but the detection accuracy is unstable at different measuring positions. This paper puts forward a two-step vibration-sound signal fusion method, in which sound signal fusion and vibration-sound signal fusion are executed respectively. The sound signals are fused through weighting to the vibration signal to reduce the influence by measuring positions, and the phase difference is eliminated by a sliding window on the time axis. Then a second fusion between the vibration signal and sound signal is conducted after normalization and superposition, and the performance of two-step fusion is compared with the existing direct fusion. Results show that the two-step fusion provides a larger signal-to-noise ratio, and the amplitudes of characteristic frequencies are also higher. A cascaded bistable stochastic resonance system is applied in the post-processing of the fusion signal to make the signal features more clear, and it is proved that the fault detection effect has an obvious improvement after the whole process. This method provides a new approach for weak fault feature detection in vibration and sound signals, and is of great significance for the maintenance of rolling bearing systems.

Driver Fatigue Detection Based On Facial Feature Analysis

2021 ◽  
pp. 2150034
Author(s):  
Yimin Zhang ◽  
Xianwei Han ◽  
Wei Gao ◽  
Yunliang Hu
Keyword(s):  
Traffic Accidents ◽  
Detection System ◽  
Recognition Rate ◽  
Ccd Camera ◽  
Facial Feature ◽  
The State ◽  
Driver Fatigue ◽  
Eyelid Closure ◽  
Fatigue Driving ◽  
Fatigue Detection

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

scholarly journals Driving Fatigue Detection from EEG Using a Modified PCANet Method

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yuliang Ma ◽  
Bin Chen ◽  
Rihui Li ◽  
Chushan Wang ◽  
Jun Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Traffic Accidents ◽  
Principal Component ◽  
Learning Model ◽  
Driving Fatigue ◽  
Fatigue Detection ◽  
Deep Learning Model

The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.

scholarly journals A System of Driving Fatigue Detection Based on Machine Vision and Its Application on Smart Device

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Wanzeng Kong ◽  
Lingxiao Zhou ◽  
Yizhi Wang ◽  
Jianhai Zhang ◽  
Jianhui Liu ◽  
...  
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Traffic Accidents ◽  
Candidate Region ◽  
Front Face ◽  
Smart Device ◽  
Adaboost Algorithm ◽  
Video Frames ◽  
Driving Fatigue ◽  
Fatigue Detection

Driving fatigue is one of the most important factors in traffic accidents. In this paper, we proposed an improved strategy and practical system to detect driving fatigue based on machine vision and Adaboost algorithm. Kinds of face and eye classifiers are well trained by Adaboost algorithm in advance. The proposed strategy firstly detects face efficiently by classifiers of front face and deflected face. Then, candidate region of eye is determined according to geometric distribution of facial organs. Finally, trained classifiers of open eyes and closed eyes are used to detect eyes in the candidate region quickly and accurately. The indexes which consist of PERCLOS and duration of closed-state are extracted in video frames real time. Moreover, the system is transplanted into smart device, that is, smartphone or tablet, due to its own camera and powerful calculation performance. Practical tests demonstrated that the proposed system can detect driver fatigue with real time and high accuracy. As the system has been planted into portable smart device, it could be widely used for driving fatigue detection in daily life.

A Multivariate Weighted Ordinal Pattern Transition Network for Characterizing Driver Fatigue Behavior from EEG Signals

2020 ◽  
Vol 30 (08) ◽  
pp. 2050118
Author(s):  
Yu-Xuan Yang ◽  
Zhong-Ke Gao
Keyword(s):  
Traffic Accidents ◽  
Fatigue Behavior ◽  
Multivariate Time Series ◽  
Detection Task ◽  
Eeg Signals ◽  
Driver Fatigue ◽  
Driving Fatigue ◽  
Fatigue Detection ◽  
Ordinal Pattern ◽  
Fatigue Mechanism

Driver fatigue has caused numerous vehicle crashes and traffic injuries. Exploring the fatigue mechanism and detecting fatigue state are of great significance for preventing traffic accidents, and further lessening economic and societal loss. Due to the objectivity of EEG signals and the availability of EEG acquisition equipment, EEG-based fatigue detection task has raised great attention in recent years. Although there exist various methods for this task, the study of fatigue mechanism and detection of fatigue state still remain much to be explored. To investigate these problems, a multivariate weighted ordinal pattern transition (MWOPT) network is proposed in this paper. To be specific, a simulated driving experiment was first conducted to obtain the EEG signals of subjects in alert state and fatigue state respectively. Then the MWOPT network is constructed based on a novel Shannon entropy. To probe into the mechanism underlying fatigue behavior, the small-worldness index is extracted from the generated MWOPT network. Furthermore, the nodal degree index is input into a classifier to distinguish the fatigue state from alert state. The obtained high accuracy indicates the effectiveness of the proposed network for EEG-based fatigue detection. Besides, four nodes are found to play an important role in identifying fatigue state. These results suggest that the proposed method enables to analyze nonlinear multivariate time series and investigate the driving fatigue behavior.

scholarly journals A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Liu ◽  
Yunfeng Ji ◽  
Yun Gao ◽  
Zhenyu Ping ◽  
Liang Kuang ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Traffic Accidents ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Warning System ◽  
Support Vector ◽  
Eyelid Closure ◽  
Recognition Result ◽  
Fatigue Driving

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

scholarly journals Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1298
Author(s):  
Nan Zhao ◽  
Dawei Lu ◽  
Kechen Hou ◽  
Meifei Chen ◽  
Xiangyu Wei ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Covariance Matrices ◽  
Temporal Information ◽  
Pressure Work ◽  
Spatial Features ◽  
Eeg Data ◽  
High Level ◽  
Fatigue Driving ◽  
Fatigue Detection ◽  
Better Than

With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.

Research on Intelligent Steering Wheel Technology Based on Multi Sensor

2014 ◽  
Vol 488-489 ◽  
pp. 1130-1133
Author(s):  
Yuan Bai ◽  
Xiao Dong Tan
Keyword(s):  
Traffic Safety ◽  
Automobile Industry ◽  
Traffic Accidents ◽  
Drunk Driving ◽  
Steering Wheel ◽  
Private Car ◽  
Driving Condition ◽  
Fatigue Driving

At present, the automobile industry is developing rapidly, the private car is widely popularized, and the hidden dangers of traffic safety exist. The phenomenon of drunk driving and fatigue driving becomes more and more serious, and the improvement for steering wheel could effectively prevent traffic accidents. This paper introduces and analyzes the intelligence of steering wheel in three major aspects, they respectively include intelligent grip detection, which tests if a driver is of fatigue driving; hart rate detection, which tests if a driver is in normal driving condition; alcohol detection, which tests if a driver drinks too much, and it predicts the possibility of accident from the drivers state, and timely gives out signal to warn the driver.

scholarly journals A novel real-time driving fatigue detection system based on wireless dry EEG

2018 ◽  
Vol 12 (4) ◽  
pp. 365-376 ◽  
Author(s):  
Hongtao Wang ◽  
Andrei Dragomir ◽  
Nida Itrat Abbasi ◽  
Junhua Li ◽  
Nitish V. Thakor ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Driving Fatigue ◽  
Fatigue Detection
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