A noninvasive real-time driving fatigue detection technology based on left prefrontal Attention and Meditation EEG

2014 ◽  
Author(s):  
Jian He ◽  
Dongdong Liu ◽  
Zhijiang Wan ◽  
Chen Hu
Keyword(s):  
Real Time ◽  
Detection Technology ◽  
Driving Fatigue ◽  
Fatigue Detection

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

Development of Detection Research on Fatigue Driving

2014 ◽  
Vol 641-642 ◽  
pp. 813-817
Author(s):  
Xiao Jun He ◽  
Jing Liu ◽  
Zhen Di Yi ◽  
Yuan Quan Yang
Keyword(s):  
Fatigue Testing ◽  
Detection Methods ◽  
Advantages And Disadvantages ◽  
Efficient Detection ◽  
Detection Technology ◽  
Driving Fatigue ◽  
Cost Efficient ◽  
Fatigue Driving ◽  
The Cost ◽  
Fatigue Detection

This paper presents the current most common fatigue-driving detection methods. The advantages and disadvantages of these detection methods are compared with. Moreover, several major products of the current fatigue detection are listed briefly. Furthermore, the development trends of driving-fatigue detection technology are prospected. The author believes that driver fatigue testing standards need to be further clarified and the non-contact detection method of driving-fatigue needs to be developed deeply. Information fusion is an important orientation for driving fatigue and we should design the cost-efficient detection products for fatigue-driving.

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 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 A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

A Novel Approach of Eye Detection Based on Haar-Like Feature and SVM

2011 ◽  
Vol 121-126 ◽  
pp. 1863-1867
Author(s):  
Yu Hang Guo ◽  
Jie Liu
Keyword(s):  
Support Vector ◽  
Expression Recognition ◽  
Generalization Capability ◽  
Eye Detection ◽  
Novel Approach ◽  
Detection Technology ◽  
Training Samples ◽  
Face Detection And Recognition ◽  
Fatigue Detection ◽  
Background Image

Recognizing the importance of eye detection technology in face detection and recognition, facial expression recognition, driver fatigue detection, and so on, the current paper presents a novel approach to eye detection in images based on AdaBoost and the Support Vector Machine (SVM). The proposed method can accurately detect eyes with different postures, facial expressions, skin colors, and glasses in a simple background image. First, a Haar-like feature AdaBoost classifier based on the developed training samples is used to detect eyes in images. An SVM post classifier trained by coordinates of the true and pseudo eye rectangles is then used to optimize the results. Experimental results show that the proposed method is fast, robust, and has high generalization capability.

scholarly journals Electroanalytical Biosensors for Circulating Tumor DNA Detection—A Brief Review

2021 ◽  
Author(s):  
Zhijia Peng ◽  
Xiaogang Lin ◽  
Weiqi Nian ◽  
Xiaodong Zheng ◽  
Jayne Wu
Keyword(s):  
Real Time ◽  
Point Of Care ◽  
Low Cost ◽  
High Sensitivity ◽  
High Specificity ◽  
Circulating Tumor Dna ◽  
Minimal Invasive ◽  
Diagnosis And Treatment ◽  
Detection Technology ◽  
Tumor Dna

Early diagnosis and treatment have always been highly desired in the fight against cancer, and detection of circulating tumor DNA (ctDNA) has recently been touted as highly promising for early cancer screening. Consequently, the detection of ctDNA in liquid biopsy gains much attention in the field of tumor diagnosis and treatment, which has also attracted research interest from the industry. However, traditional gene detection technology is difficult to achieve low cost, real-time and portable measurement of ctDNA. Electroanalytical biosensors have many unique advantages such as high sensitivity, high specificity, low cost and good portability. Therefore, this review aims to discuss the latest development of biosensors for minimal-invasive, rapid, and real-time ctDNA detection. Various ctDNA sensors are reviewed with respect to their choices of receptor probes, detection strategies and figures of merit. Aiming at the portable, real-time and non-destructive characteristics of biosensors, we analyze their development in the Internet of Things, point-of-care testing, big data and big health.

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