Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This imbalance biases the model toward routine activities and weakens its sensitivity to true fall events. To address this challenge, we propose a personalization framework that combines semi-supervised clustering with contrastive learning to identify and balance the most informative user feedback samples. The framework is evaluated under three retraining strategies, including Training from Scratch (TFS), Transfer Learning (TL), and Few-Shot Learning ...
| Authors | Awatif Yasmin, Tarek Mahmud, Sana Alamgeer et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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