Recent Advances in Fatigue Detection Algorithm Based on EEG

Fatigue is a state commonly caused by overworked,which seriously affects daily work and life.How to detect mental fatigue has always been a hot spot for researchers to explore.Electroencephalogram(EEG)is considered one of the most accurate and objective indicators.This article investigated the devel-opment of classification algorithms applied in EEG-based fatigue detection in recent years.According to the different source of the data,we can divide these classification algorithms into two categories,intra-subject(within the same sub-ject)and cross-subject(across different subjects).In most studies,traditional machine learning algorithms with artificial feature extraction methods were com-monly used for fatigue detection as intra-subject algorithms.Besides,deep learn-ing algorithms have been applied to fatigue detection and could achieve effective result based on large-scale dataset.However,it is difficult to perform long-term calibration training on the subjects in practical applications.With the lack of large samples,transfer learning algorithms as a cross-subject algorithm could promote the practical application of fatigue detection methods.We found that the research based on deep learning and transfer learning has gradually increased in recent years.But as afield with increasing requirements,researchers still need to con-tinue to explore efficient decoding algorithms,design effective experimental para-digms,and collect and accumulate valid standard data,to achieve fast and accurate fatigue detection methods or systems to further widely apply.

Research on Facial Fatigue Detection of Drivers with Multi-feature Fusion

In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms.

Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion

Real-time detection of driver fatigue status is of great significance for road traffic safety.In this paper,a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock.The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard.The landmarks of the driver’s face were labeled and the eye-area was segmented.By calculating the aspect ratios of the eyes,the duration of eye closure,frequency of blinks and PERCLOS of both colored and infrared,fatigue can be detected.Based on the change of light intensity detected by a photosensitive device,the weight matrix of the colored features and the infrared features was adjusted adaptively to reduce the impact of lighting on fatigue detection.Video samples of the driver’s face were recorded in the test vehicle.After training the classification model,the results showed that our method has high accuracy on driver fatigue detection in both daytime and nighttime.

Color space lip segmentation for drivers＇ fatigue detection

to the chroma distribution diversity （CDD） between lip color and skin color, the lip color area is segmented by the back propagation neural network （BPNN） with three typical color features. Isolated noisy points of the lip color area in binary image are eliminated by a proposed re- gion connecting algorithm. An improved integral projection algorithm is presented to locate the lip boundary. Whether a driver is fatigued is recognized by the ratio of the frame number of the images with mouth opening continuously to the total image frame number in every 20s. The experiments show that the proposed algorithm provides higher correct rate and reliability for fatigue driving detec- tion, and is superior to the single color feature-based method in the lip color segmention. Besides, it improves obviously the accuracy and speed of the lip boundary location compared with the traditional integral projection algrothm.

Vision-based fatigue crack detection using global motion compensation and video feature tracking

Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural integrity.Despite being the most common practice for fatigue crack detection,human visual inspection is known to be labor intensive,time-consuming,and prone to error.In this study,a computer vision-based fatigue crack detection approach using a short video recorded under live loads by a moving consumer-grade camera is presented.The method detects fatigue crack by tracking surface motion and identifies the differential motion pattern caused by opening and closing of the fatigue crack.However,the global motion introduced by a moving camera in the recorded video is typically far greater than the actual motion associated with fatigue crack opening/closing,leading to false detection results.To overcome the challenge,global motion compensation(GMC)techniques are introduced to compensate for camera-induced movement.In particular,hierarchical model-based motion estimation is adopted for 2D videos with simple geometry and a new method is developed by extending the bundled camera paths approach for 3D videos with complex geometry.The proposed methodology is validated using two laboratory test setups for both in-plane and out-of-plane fatigue cracks.The results confirm the importance of motion compensation for both 2D and 3D videos and demonstrate the effectiveness of the proposed GMC methods as well as the subsequent crack detection algorithm.

A new real-time eye tracking based on nonlinear unscented Kalman filter for monitoring driver fatigue

A new scheme for driver fatigue detection is presented, which is based on the nonlinear unscented Kalman filter and eye tracking. Assuming a probability distribution than to approximate an arbitrary nonlinear function or transformation, eye nonlinear tracking can be achieved using an unscented transformation (UT), which adopts a set of deterministic sigma points to match the posterior probability density function of the eye movement. Driver fatigue can be detected using the percentage of eye closure (PERCLOS) framework in a realistic driving condition after the eye nonlinear tracking. This system was tested adequately in realistic driving environments with subjects of different genders, with/without glasses, in day/night driving, being commercial/noncommercial drivers, in continuous driving time, and under different road conditions. The last experimental results show that the proposed method not only improves the robustness for nonlinear eye tracking, but also can provide more accurate estimation than the traditional Kalman filter.

An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

EEG processing and its application in brain-computer interface

Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.

Research on Driver Monitoring Systems Based on Vital Signs and Behavior Detection

Aiming at drivers’dangerous driving behavior monitoring and health monitoring,this paper designs an intelligent steering wheel that can monitor dan-gerous driving behavior and a steering wheel sleeve that can monitor physical health.The MTCNN model is primarily used to obtain a driver’s face image in real time.The PFLD algorithm was used to obtain the facial model positioning feature points,and the degree of driver fatigue was determined by combining the relevant parameters.The fatigue algorithm proposed in this paper can improve the effectiveness and accuracy of monitoring.Then,according to the LSTM network model,11 groups of key point information of the human body are obtained,and the human motion track is identified and then combined with the facial information to complete the judgment of driving behavior such as drinking water,smoking and walking.Through the PPG and ECG fusion algorithm based on LSTM,the reliability of the system to collect vital signs such as body temperature,blood pressure,heart rate and blood oxygen of the driver is improved.It was determined that the system could monitor a driver’s driving behavior in real time and consider its health management.

Fatigue driving detection based on Haar feature and extreme learning machine

As the significant branch of intelligent vehicle networking technology, the intelligent fatigue driving detection technology has been introduced into the paper in order to recognize the fatigue state of the vehicle driver and avoid the traffic accident. The disadvantages of the traditional fatigue driving detection method have been pointed out when we study on the traditional eye tracking technology and traditional artificial neural networks. On the basis of the image topological analysis technology, Haar like features and extreme learning machine algorithm, a new detection method of the intelligent fatigue driving has been proposed in the paper. Besides, the detailed algorithm and realization scheme of the intelligent fatigue driving detection have been put forward as well. Finally, by comparing the results of the simulation experiments, the new method has been verified to have a better robustness, efficiency and accuracy in monitoring and tracking the drivers＇ fatigue driving by using the human eye tracking technology.

A comprehensive review of approaches to detect fatigue using machine learning techniques

In the past decades,there have been numerous advancements in the field of technology.This has led to many scientific breakthroughs in the field of medical sciences.In this,rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent.So,this study aimed to understand what is fatigue,its repercussions,and techniques to detect it using machine learning(ML)approaches.This paper introduces,discusses methods and recent advancements in the field of fatigue detection.Further,we categorized the methods that can be used to detect fatigue into four diverse groups,that is,mathematical models,rule-based implementation,ML,and deep learning.This study presents,compares,and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue.Finally,the paper discusses the possible areas for improvement.