Explainable Detection of Alzheimer's Disease Through Analysis of Human Behavior in Video

Current diagnostic methods for Alzheimer's disease (AD), such as brain imaging and cognitive impairment questionnaires, are costly and time-consuming, making early detection challenging. In this work, we develop a computer vision-based method to detect AD using behavioral data collected from th...

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Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors Huang, Bao-Hsuan, Kuo, Po-Chih, Huang, Likai, Hu, Chaur-Jong, Chen, Cheng-Yu
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.04.2025
Subjects
Alzheimer's disease
Behavioral sciences
Computational modeling
Cookie Theft
Grad-CAM
Medical diagnostic imaging
Medical services
Neuroimaging
SHAP
Signal processing
Speech processing
Support vector machines
Time Up and Go
Turning
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Summary:Current diagnostic methods for Alzheimer's disease (AD), such as brain imaging and cognitive impairment questionnaires, are costly and time-consuming, making early detection challenging. In this work, we develop a computer vision-based method to detect AD using behavioral data collected from the Timed Up and Go (TUG) test and the Cookie Theft (CT) picture description task. By analyzing body joints and facial landmarks through Convolutional Neural Networks and Support Vector Machines, we classified subjects into AD and Non-AD categories across four subtasks: Walking, Sit-Stand, Turning, and Describing. Our approach achieved an F1-score of 0.92±0.03, demonstrating the potential of video-based analysis for AD detection. To enhance the explainability of our model, we applied model explanation methods, identifying key features and symptoms involved in the decision-making process.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10888996