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Demo Abstract: AD-CLIP: Privacy-Preserving, Low-Cost Synthetic Human Action Dataset for Alzheimer’s Patients via CLIP-based Models
Heming Fu (Chinese University of Hong Kong), Hongkai Chen (Chinese University of Hong Kong), Guoliang Xing (Chinese University of Hong Kong)
Résumé : With the increasing demand for smart health applications that emphasize privacy and efficiency, we introduce AD-CLIP, a synthetic data generation framework using CLIP-based models for Alzheimer’s patients. Leveraging the public dataset and data we collected from Alzheimer’s patients, AD-CLIP synthesizes human action videos featuring Alzheimer’s disease. To address privacy concerns, labeling cost, and imbalanced data distribution, AD-CLIP generates a comprehensive labeled human action skeleton dataset from depth cameras with balanced data distribution. Our preliminary experiments confirm the effectiveness of the synthesized dataset by improving the accuracy of human activity recognition up to 76.56%, which demonstrates AD-CLIP’s potential to enhance smart health applications.