用对比学习筛选关键反馈数据，提升个性化跌倒检测准确率
Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
- 通过对比学习挑选最具信息量的用户反馈样本
- 训练从零开始方法达25%性能提升，少样本学习也提升7%
- 适合需要高精度个性化跌倒检测的智能穿戴场景
个性化跌倒检测模型可通过适配个体运动模式显著提升准确率，但实际效果常受限于真实跌倒数据稀缺和非跌倒反馈样本占主导。这种失衡使模型偏向日常活动，降低对真实跌倒事件的敏感性。为此，我们提出一种结合半监督聚类与对比学习的个性化框架，用于识别并平衡最具信息量的用户反馈样本。在三种重训练策略（从零训练、迁移学习、少样本学习）下评估该框架的适应性。十名参与者的实时实验表明，从零训练方法表现最优，较基线最高提升25%；少样本学习次之，提升7%，验证了选择性个性化在真实部署中的有效性。
原文摘要 · Abstract (English)
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 (FSL), to assess adaptability across learning paradigms. Real-time experiments with ten participants show that the TFS approach achieves the highest performance, with up to a 25% improvement over the baseline, while FSL achieves the second-highest performance with a 7% improvement, demonstrating the effectiveness of selective personalization for real-world deployment.
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