A Noninvasive Real-Time Solution for Driving Fatigue Detection Based on Left Prefrontal EEG and Eye Blink

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.

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Real-Time Head Detection with Kinect for Driving Fatigue Detection

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-42051-1_74 ◽

2013 ◽

pp. 600-607 ◽

Cited By ~ 2

Author(s):

Yang Cao ◽

Bao-Liang Lu

Keyword(s):

Real Time ◽

Head Detection ◽

Driving Fatigue ◽

Fatigue Detection

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Real-Time Fatigue Analysis of Driver through Iris Recognition

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i6.pp3306-3312 ◽

2017 ◽

Vol 7 (6) ◽

pp. 3306

Author(s):

Gopalakrishna K ◽

Hariprasad S.A.

Keyword(s):

Real Time ◽

Mental Status ◽

Iris Recognition ◽

Accurate Method ◽

Fatigue Analysis ◽

Heart Beat ◽

Road Accidents ◽

Eye Blink ◽

Status Analysis ◽

Fatigue Detection

In recent days, the driver’s fault accounted for about 77.5% of the total road accidents that are happening every day. There are several methods for the driver’s fatigue detection. These are based on the movement of the eye ball using eye blinking sensor, heart beat measurement using Electro Cardio Gram, mental status analysis using ElectroEncephaloGram, muscle cramping detection, etc. However the above said methods are more complicated and create inconvenience for the driver to drive the vehicle. Also, these methods are less accurate. In this work, an accurate method is adopted to detect the driver’s fatigue based on status of the eyes using Iris recognition and the results shows that the proposed method is more accurate (about 80%) compared to the existing methods such as Eye blink Sensor method.

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Performance Analysis of Real-Time Eye Blink Detector for Varying Lighting Conditions and User Distance from the Camera

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v5i12.3540 ◽

2017 ◽

Vol 5 (12) ◽

pp. 35-40

Author(s):

Hari Singh ◽

◽

Jaswinder Singh

Keyword(s):

Performance Analysis ◽

Real Time ◽

Eye Blink ◽

Lighting Conditions

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

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0191 ◽

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.

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