AIMC Topic: Eye-Tracking Technology
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Smartphone eye-tracking with deep learning: Data quality and field testing.
Behavior research methods
Jun 18, 2025
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...
Emergence of human-like attention and distinct head clusters in self-supervised vision transformers: A comparative eye-tracking study.
Neural networks : the official journal of the International Neural Network Society
May 21, 2025
Visual attention models aim to predict human gaze behavior, yet traditional saliency models and deep gaze prediction networks face limitations. Saliency models rely on handcrafted low-level visual features, often failing to capture human gaze dynamic...
PhyTransformer: A unified framework for learning spatial-temporal representation from physiological signals.
Neural networks : the official journal of the International Neural Network Society
May 19, 2025
As a modal of physiological information, electroencephalogram (EEG), surface electromyography (sEMG), and eye tracking (ET) signals are widely used to decode human intention, promoting the development of human-computer interaction systems. Extensive ...
FovealNet: Advancing AI-Driven Gaze Tracking Solutions for Efficient Foveated Rendering in Virtual Reality.
IEEE transactions on visualization and computer graphics
Apr 25, 2025
Leveraging real-time eye tracking, foveated rendering optimizes hardware efficiency and enhances visual quality virtual reality (VR). This approach leverages eye-tracking techniques to determine where the user is looking, allowing the system to rende...
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
Apr 22, 2025
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
Apr 10, 2025
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...
LEyes: A lightweight framework for deep learning-based eye tracking using synthetic eye images.
Behavior research methods
Mar 31, 2025
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...
Iris Geometric Transformation Guided Deep Appearance-Based Gaze Estimation.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Mar 11, 2025
The geometric alterations in the iris's appearance are intricately linked to the gaze direction. However, current deep appearance-based gaze estimation methods mainly rely on latent feature sharing to leverage iris features for improving deep represe...
High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis.
Biosensors
Feb 14, 2025
An effective and highly accurate strabismus screening method is expected to identify potential patients and provide timely treatment to prevent further deterioration, such as amblyopia and even permanent vision loss. To satisfy this need, this work s...
Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.
Scientific data
Feb 5, 2025
Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces the adenoma miss rate (AMR). However, few studies address the critical issue of endoscopist-AI collab...
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