scholarly journals A Fatigue Driving Detection Algorithm Based on Facial Motion Information Entropy

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Feng You ◽  
Yunbo Gong ◽  
Haiqing Tu ◽  
Jianzhong Liang ◽  
Haiwei Wang
Keyword(s):  
Real Time ◽  
Traffic Safety ◽  
Information Entropy ◽  
Detection Algorithm ◽  
Facial Motion ◽  
Facial Information ◽  
Video Frames ◽  
Fatigue Driving ◽  
Fatigue Detection ◽  
Better Than

Research studies on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms asses the driving state according to limited video frames, thus resulting in some inaccuracy. We propose a real-time detection algorithm involved in information entropy. Particularly, this algorithm relies on the analysis of sufficient consecutive video frames. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, we construct a geometric area called Face Feature Triangle (FFT) based on the application of the Dlib toolkit as well as the landmarks and the coordinates of the facial regions; then we create a Face Feature Vector (FFV), which contains all the information of the area and centroid of each FFT. We use FFV as an indicator to determine whether the driver is in fatigue state. Finally, we design a sliding window to get the facial information entropy. Comparative experiments show that our algorithm performs better than the current ones on both accuracy and real-time performance. In simulated driving applications, the proposed algorithm detects the fatigue state at a speed of over 20 fps with an accuracy of 94.32%.

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.

scholarly journals Application Analysis of Wearable Technology and Equipment Based on Artificial Intelligence in Volleyball

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xianyan Dai ◽  
Shangbin Li
Keyword(s):  
Real Time ◽  
Sports Injuries ◽  
Recognition Performance ◽  
Wearable Technology ◽  
Detection Algorithm ◽  
The Body ◽  
Self Assessment ◽  
Ecg Signals ◽  
Exercise Fatigue ◽  
Fatigue Detection

Today, while people’s satisfaction with materials is high, the pursuit of health has begun and sports are becoming increasingly important. Volleyball is a good physical and mental exercise, which helps improve the health of the body. However, excessive exercise usually leads to muscle strain and more serious accidents. Therefore, how to effectively prevent excessive fatigue and sports injuries becomes more and more important. In the past, some methods of exercise fatigue detection were mostly self-assessment through some indicators, which lacked real-time and accuracy. With the advancement of smart technology, in order to better detect sports fatigue, smart wearable technology and equipment are used in volleyball. Firstly, surface electromyography signals (sEMG) are collected through wearable technology and equipment. Secondly, the signal is preprocessed to extract features that are conducive to exercise fatigue assessment. Finally, a motion fatigue detection algorithm is designed to identify and classify features and evaluate the motion status in real-time. The simulation results show that it is feasible to collect ECG signals and EMG signals to detect exercise fatigue. The algorithm has good recognition performance, can evaluate exercise conditions in real-time, and prevent fatigue and injury during exercise.

scholarly journals Driver Fatigue Detection Based on Facial Key Points and LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Long Chen ◽  
Guojiang Xin ◽  
Yuling Liu ◽  
Junwei Huang
Keyword(s):  
Traffic Safety ◽  
Short Term Memory ◽  
Field Research ◽  
Recognition Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
State Recognition ◽  
Key Points ◽  
Fatigue Driving ◽  
Fatigue Detection

In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature vector of each frame. The fatigue feature vectors of multiple frames are spliced into a temporal feature sequence and sent to the LSTM (long short-term memory) network to obtain a final fatigue feature value. Experiments show that compared with other methods, the fatigue state recognition algorithm proposed in this paper has achieved better results in accuracy. The average accuracy of the proposed method in detecting key points of the face is as high as 93%, and the running time is less than half of the ordinary DLIB method.

scholarly journals Smart water consumption measurement system for houses using IoT and cloud computing

2020 ◽  
Vol 192 (9) ◽  
Author(s):  
Henry Fuentes ◽  
David Mauricio
Keyword(s):  
Real Time ◽  
Water Consumption ◽  
Historical Context ◽  
Leak Detection ◽  
Detection Algorithm ◽  
Web Interface ◽  
Smart Water ◽  
Margin Of Error ◽  
Water Measurement ◽  
Better Than

Abstract Presently, in several parts of the world, water consumption is not measured or visualized in real time, in addition, water leaks are not detected in time and with high precision, generating unnecessary waste of water. That is why this article presents the implementation of a smart water measurement consumption system under an architecture design, with high decoupling and integration of various technologies, which allows real-time visualizing the consumptions, in addition, a leak detection algorithm is proposed based on rules, historical context, and user location that manages to cover 10 possible water consumption scenarios between normal and anomalous consumption. The system allows data to be collected by a smart meter, which is preprocessed by a local server (Gateway) and sent to the Cloud from time to time to be analyzed by the leak detection algorithm and, simultaneously, be viewed on a web interface. The results show that the algorithm has 100% Accuracy, Recall, Precision, and F1 score to detect leaks, far better than other procedures, and a margin of error of 4.63% recorded by the amount of water consumed.

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.

scholarly journals Fatigue driving detection based on electrooculography: a review

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yuanyuan Tian ◽  
Jingyu Cao
Keyword(s):  
Monitoring System ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Feature Fusion ◽  
State Of The Art ◽  
Low Cost ◽  
Advantages And Disadvantages ◽  
Art Research ◽  
Fatigue Driving ◽  
Better Than

AbstractTo accurately identify fatigued driving, establishing a monitoring system is one of the important guarantees of improving traffic safety and reducing traffic accidents. Among many research methods, electrooculogram signal (EOG) has unique advantages. This paper presents a systematic literature review of these technologies and summarizes a basic framework of fatigue driving monitoring system based on EOGs. Then we summarize the advantages and disadvantages of existing technologies. In addition, 80 primary references published during the last decade were identified. The multi-feature fusion technique based on EOGs performs better than other traditional methods due to its low cost, low power consumption and low intrusion, while its application is still limited which needs more efforts to obtain good and generalizable results. And then, an overview of the literature on technology is given, revealing a premier and unbiased survey of the existing empirical research of classification techniques that have been applied to fatigue driving analysis. Finally, this paper adds value to the current literature by investigating the application of EOG signals in fatigued driving and the design of related systems, future guidelines have been provided to practitioners and researchers to grasp the major contributions and challenges in the state-of-the-art research.

scholarly journals Driver Fatigue Detection Based on Convolutional Neural Networks Using EM-CNN

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zuopeng Zhao ◽  
Nana Zhou ◽  
Lan Zhang ◽  
Hualin Yan ◽  
Yi Xu ◽  
...  
Keyword(s):  
Mouth Opening ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Feature Points ◽  
Driver Fatigue ◽  
Convolutional Network ◽  
Eyelid Closure ◽  
Two Parameters ◽  
Fatigue Driving ◽  
Fatigue Detection

With a focus on fatigue driving detection research, a fully automated driver fatigue status detection algorithm using driving images is proposed. In the proposed algorithm, the multitask cascaded convolutional network (MTCNN) architecture is employed in face detection and feature point location, and the region of interest (ROI) is extracted using feature points. A convolutional neural network, named EM-CNN, is proposed to detect the states of the eyes and mouth from the ROI images. The percentage of eyelid closure over the pupil over time (PERCLOS) and mouth opening degree (POM) are two parameters used for fatigue detection. Experimental results demonstrate that the proposed EM-CNN can efficiently detect driver fatigue status using driving images. The proposed algorithm EM-CNN outperforms other CNN-based methods, i.e., AlexNet, VGG-16, GoogLeNet, and ResNet50, showing accuracy and sensitivity rates of 93.623% and 93.643%, respectively.

scholarly journals Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study

2020 ◽  
Vol 65 (4) ◽  
pp. 461-468
Author(s):  
Jannatul Naeem ◽  
Nur Azah Hamzaid ◽  
Amelia Wong Azman ◽  
Manfred Bijak
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Isometric Force ◽  
Mean Square ◽  
Clear Indication ◽  
Initial Value ◽  
Cord Injury ◽  
The Mean ◽  
Electrical Stimulator ◽  
Fatigue Detection

AbstractFunctional electrical stimulation (FES) has been used to produce force-related activities on the paralyzed muscle among spinal cord injury (SCI) individuals. Early muscle fatigue is an issue in all FES applications. If not properly monitored, overstimulation can occur, which can lead to muscle damage. A real-time mechanomyography (MMG)-based FES system was implemented on the quadriceps muscles of three individuals with SCI to generate an isometric force on both legs. Three threshold drop levels of MMG-root mean square (MMG-RMS) feature (thr50, thr60, and thr70; representing 50%, 60%, and 70% drop from initial MMG-RMS values, respectively) were used to terminate the stimulation session. The mean stimulation time increased when the MMG-RMS drop threshold increased (thr50: 22.7 s, thr60: 25.7 s, and thr70: 27.3 s), indicating longer sessions when lower performance drop was allowed. Moreover, at thr70, the torque dropped below 50% from the initial value in 14 trials, more than at thr50 and thr60. This is a clear indication of muscle fatigue detection using the MMG-RMS value. The stimulation time at thr70 was significantly longer (p = 0.013) than that at thr50. The results demonstrated that a real-time MMG-based FES monitoring system has the potential to prevent the onset of critical muscle fatigue in individuals with SCI in prolonged FES sessions.

scholarly journals An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

2021 ◽  
Vol 13 (12) ◽  
pp. 2259
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Qing Zhao ◽  
Zihan Peng ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
Sampling Rate ◽  
Threshold Value ◽  
Deformation Monitoring ◽  
Success Rates ◽  
Multipath Mitigation ◽  
Mode Decomposition ◽  
Observation Noise ◽  
Original Observation ◽  
Better Than

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

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