SAFER-Activities：用于跌倒事件与日常活动智能评估的数据集

中文摘要 AI 辅助

智能医疗监控系统需要精确的动作识别，以确保行动不便人群的福祉，并在跌倒等危急情况下及时干预。现有数据集通常基于片段，缺乏在线识别动作所需的帧级细节。为解决这一问题，我们引入了SAFER-Activities，一个用于跌倒检测和身体活动监控的数据集，并设有专门的轮椅使用场景子集。该数据集包含由多个摄像头捕获的超过66小时的视频数据，包含85,310个动作实例和30个动作类别的帧级标注。我们使用2D和3D骨架模型、具有冻结骨干的RGB模型以及多模态融合策略，对SAFER-Activities上的动作识别进行了基准测试，并在实验室内、分布外和跨数据集测试集上进行了评估。基于骨架的模型在领域偏移下泛化最佳；将冻结的RGB特征与骨架流融合可改善域内识别，优于基线CNN1D，在轮椅子集上最为明显，但在分布外情况下性能下降。跨数据集和定性评估证实，在SAFER-Activities上训练的模型能很好地迁移到未见环境和外部跌倒数据。为支持稳健跌倒检测和活动监控的研究，我们在该https URL发布了数据集和代码。

英文摘要

Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely intervention in critical situations such as falls, particularly for mobility-challenged individuals. Existing datasets are often clip-based, lacking the frame-level detail needed to recognize actions online, as they unfold. To address this, we introduce SAFER-Activities, a dataset for fall detection and physical activity monitoring, with a dedicated subset for wheelchair use scenarios. It comprises over 66 hours of video data captured by multiple cameras, with 85,310 action instances and frame-level annotations for 30 action classes. We benchmark action recognition on SAFER-Activities with 2D and 3D skeleton models, RGB models with frozen backbones, and multimodal fusion strategies, and evaluate on in-lab, out-of-distribution, and cross-dataset test sets. Skeleton-based models generalize best under domain shift; fusing frozen RGB features with the skeleton stream improves in-domain recognition over the baseline CNN1D, most clearly on the wheelchair subset, but degrades out of distribution. Cross-dataset and qualitative evaluations confirm that models trained on SAFER-Activities transfer well to unseen environments and external fall data. To support research on robust fall detection and activity monitoring, we release the dataset and code at https://safer-activities.github.io/.

发表机构

• KU Leuven（鲁汶大学）
• Asian Institute of Technology（亚洲理工学院）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

Accepted to ECCV 2026