GazeMap: Dual-Pathway CNN Approach for Diagnosing Alzheimer’s Disease from Gaze and Head Movements

Hyuntaek Jung, Shinwoo Ham, Hyunyoung Kil, Jung Eun Shin () and Eun Yi Kim ()
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Hyuntaek Jung: Computer Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
Shinwoo Ham: Computer Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
Hyunyoung Kil: Department of Software, Korea Aerospace University, Goyang 10540, Republic of Korea
Jung Eun Shin: Department of Otolaryngology-Head & Neck Surgery, College of Medicine, Konkuk University, Seoul 05030, Republic of Korea
Eun Yi Kim: Computer Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea

Mathematics, 2025, vol. 13, issue 11, 1-18

Abstract: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs cognitive function, making early detection crucial for timely intervention. This study proposes a novel AD detection framework integrating gaze and head movement analysis via a dual-pathway convolutional neural network (CNN). Unlike conventional methods relying on linguistic, speech, or neuroimaging data, our approach leverages non-invasive video-based tracking, offering a more accessible and cost-effective solution to early AD detection. To enhance feature representation, we introduce GazeMap, a novel transformation converting 1D gaze and head pose time-series data into 2D spatial representations, effectively capturing both short- and long-term temporal interactions while mitigating missing or noisy data. The dual-pathway CNN processes gaze and head movement features separately before fusing them to improve diagnostic accuracy. We validated our framework using a clinical dataset (112 participants) from Konkuk University Hospital and an out-of-distribution dataset from senior centers and nursing homes. Our method achieved 91.09% accuracy on in-distribution data collected under controlled clinical settings, and 83.33% on out-of-distribution data from real-world scenarios, outperforming several time-series baseline models. Model performance was validated through cross-validation on in-distribution data and tested on an independent out-of-distribution dataset. Additionally, our gaze-saliency maps provide interpretable visualizations, revealing distinct AD-related gaze patterns.

Keywords: Alzheimer’s disease detection; GazeMap; gaze tracking; head movement; representation learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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