ROAD ACCIDENTAL ALERT SYSTEM BASED ON LANE CHANGE DETECTION AND EYE BLINK DETECTION

This paper reports on a mask-type sensor for simultaneous pulse wave and respiration measurements and eye blink detection that uses only one sensing element. In the proposed sensor, a flexible air bag-shaped chamber whose inner pressure change can be measured by a microelectromechanical system-based piezoresistive cantilever was used as the sensing element. The air bag-shaped chamber is fabricated by wrapping a sponge pad with plastic film and polyimide tape. The polyimide tape has a hole to which the substrate with the piezoresistive cantilever adheres. By attaching the sensor device to a mask where it contacts the nose of the subject, the sensor can detect the pulses and eye blinks of the subject by detecting the vibration and displacement of the nose skin caused by these physiological parameters. Moreover, the respiration of the subject causes pressure changes in the space between the mask and the face of the subject as well as slight vibrations of the mask. Therefore, information about the respiration of the subject can be extracted from the sensor signal using either the low-frequency component (<1 Hz) or the high-frequency component (>100 Hz). This paper describes the sensor fabrication and provides demonstrations of the pulse wave and respiration measurements as well as eye blink detection using the fabricated sensor.

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mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation

Companion Publication of the 2020 International Conference on Multimodal Interaction ◽

10.1145/3395035.3425257 ◽

2020 ◽

Cited By ~ 1

Author(s):

Roberto Daza ◽

Aythami Morales ◽

Julian Fierrez ◽

Ruben Tolosana

Keyword(s):

Eye Blink ◽

Blink Detection ◽

Attention Level

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Embedded System for Eye Blink Detection Using Machine Learning Technique

2021 1st Babylon International Conference on Information Technology and Science (BICITS) ◽

10.1109/bicits51482.2021.9509908 ◽

2021 ◽

Author(s):

Bishar R. Ibrahim ◽

Farhad M. Khalifa ◽

Subhi R. M. Zeebaree ◽

Nashwan A. Othman ◽

Ahmed Alkhayyat ◽

...

Keyword(s):

Machine Learning ◽

Embedded System ◽

Machine Learning Technique ◽

Eye Blink ◽

Learning Technique ◽

Blink Detection

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Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm

European Journal of Engineering and Technology Research ◽

10.24018/ejers.2021.6.4.2438 ◽

2021 ◽

Vol 6 (4) ◽

pp. 6-12

Author(s):

Dang-Khoa Tran ◽

Thanh-Hai Nguyen ◽

Thanh-Nghia Nguyen

Keyword(s):

Detection Algorithm ◽

Time Frequency ◽

Eye Blink ◽

Eye Blinks ◽

User Training ◽

Electrical Potentials ◽

Ocular Artifact ◽

Blink Detection ◽

Thresholding Algorithm ◽

Frequency Properties

In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.

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