AIMC Topic: Eye Movements

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Smartphone eye-tracking with deep learning: Data quality and field testing.

Behavior research methods
Eye-tracking is widely used to measure human attention in research, commercial, and clinical applications. With the rapid advancements in artificial intelligence and mobile computing, deep learning algorithms for computer vision-based eye tracking ha...

Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity.

Scientific reports
Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependenc...

Cognitive Load Prediction From Multimodal Physiological Signals Using Multiview Learning.

IEEE journal of biomedical and health informatics
Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification,...

A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to the overlap of initial symptoms with normal aging processes. Given that no cure exists and current ...

Classification of schizophrenia based on RAnet-ET: resnet based attention network for eye-tracking.

Journal of neural engineering
There is a notable need of quantifiable and objective methods for the classification of schizophrenia. Patients with schizophrenia exhibit atypical eye movements compared with healthy individuals. To address this need, we have developed a classificat...

The potential of evaluating shape drawing using machine learning for predicting high autistic traits.

PloS one
BACKGROUND: Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product...

LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images.

Behavior research methods
Deep learning methods have significantly advanced the field of gaze estimation, yet the development of these algorithms is often hindered by a lack of appropriate publicly accessible training datasets. Moreover, models trained on the few available da...

Automated Autism Assessment With Multimodal Data and Ensemble Learning: A Scalable and Consistent Robot-Enhanced Therapy Framework.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Navigating the complexities of Autism Spectrum Disorder (ASD) diagnosis and intervention requires a nuanced approach that addresses both the inherent variability in therapeutic practices and the imperative for scalable solutions. This paper presents ...

Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

Resuscitation
AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. ...

Eye-gesture control of computer systems via artificial intelligence.

F1000Research
BACKGROUND: Artificial Intelligence (AI) offers transformative potential for human-computer interaction, particularly through eye-gesture recognition, enabling intuitive control for users and accessibility for individuals with physical impairments.