Self-supervised contrastive learning for fall detection using 3D vision-based body articulation
This paper presents a mathematical modeling approach for fall detection using a 3D vision-based contrastive learning framework. Traditional models struggle with high false positives and poor generalization across environments. To address this, we propose a self-supervised contrastive learning model that maps 3D skeletal motion sequences into a low-dimensional embedding space, optimizing feature separation between falls and non-falls. Our method employs spatial-temporal modeling and a contrastive loss function based on cosine similarity to enhance discrimination. By