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

Abstract

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.