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Blinking

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Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety.

Sensors (Basel, Switzerland)
Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has ...

Deep learning models for webcam eye tracking in online experiments.

Behavior research methods
Eye tracking is prevalent in scientific and commercial applications. Recent computer vision and deep learning methods enable eye tracking with off-the-shelf webcams and reduce dependence on expensive, restrictive hardware. However, such deep learning...

Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection.

IEEE transactions on neural networks and learning systems
Computer vision syndrome causes vision problems and discomfort mainly due to dry eye. Several studies show that dry eye in computer users is caused by a reduction in the blink rate and an increase in the prevalence of incomplete blinks. In this conte...

Identifying subgroups of urge suppression in Obsessive-Compulsive Disorder using machine learning.

Journal of psychiatric research
Obsessive-compulsive disorder (OCD) is phenomenologically heterogeneous. While predominant models suggest fear and harm prevention drive compulsions, many patients also experience uncomfortable sensory-based urges ("sensory phenomena") that may be as...

[Practical Application of Intelligent Vision Measurement System Based on Deep Learning].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
To comprehensively assess the true visual function of clinical dry eye patients and the comprehensive impact of blinking characteristics on functional vision of the human eye, an intelligent vision measurement system has been designed and developed t...

Eye-Rubbing Detection Using a Smartwatch: A Feasibility Study Demonstrated High Accuracy With Machine Learning.

Translational vision science & technology
PURPOSE: In this work, we present a new machine learning method based on the transformer neural network to detect eye rubbing using a smartwatch in a real-life setting. In ophthalmology, the accurate detection and prevention of eye rubbing could redu...

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if tra...

Dynamic blinking feature extraction for automated facial nerve paralysis detection.

Computers in biology and medicine
Facial nerve paralysis (FNP) impair eyelid closure and blinking, risking ophthalmic complications and vision loss. Current detection methods primarily rely on static facial asymmetries, overlooking the dynamic eyelid movements during blinking that ar...

Assessment of PTSD in military personnel via machine learning based on physiological habituation in a virtual immersive environment.

Scientific reports
Posttraumatic stress disorder (PTSD) is a complex mental health condition triggered by exposure to traumatic events that leads to physical health problems and socioeconomic impairments. Although the complex symptomatology of PTSD makes diagnosis diff...

A CNN-based approach for detecting eye blink episodes in EEG signals.

Journal of neural engineering
This study aims to develop and evaluate a convolutional neural network (CNN)-based architecture for detecting eye blink episodes in electroencephalographic (EEG) signals, with a focus on the precise detection of individual events rather than their cl...