发表机构
CESI LINEACT; ENSAM（CESI LINEACT; 国立高等先进技术学校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对跌倒事件中的撞击检测难题，利用时空图卷积网络、GRU和BiLSTM层处理3D骨架数据，能精准区分假跌倒与实际撞击，提高精度，在改进数据集上准确率超90%，并公开数据集助力后续研究。
AI 中文摘要
跌倒是65岁以上人群意外死亡的重大风险，是全球健康问题。跌倒指人失去平衡并移动到非站立位置，可能导致撞击地面。虽然跌倒检测系统总体效果良好，但跌倒中的撞击检测仍具挑战性。本研究提出一种有效方法，通过时空图卷积网络（STGCN）、门控循环单元（GRU）和双向长短期记忆（BiLSTM）层，将3D关节骨架数据视为图来准确检测跌倒事件中的撞击。通过确定撞击时刻，该方法区分假跌倒和实际撞击以提高精度，有助于更好地分配医疗资源。使用改进的3D骨架UP-Fall数据集评估，该方法在各种跌倒场景中准确率超过90%。还公开了改进数据集以促进进一步研究。
英文摘要
Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any event where a person loses balance and moves to an off-position, which may or may not result in an impact where the person hits the ground. While fall detection systems have achieved good results in general, impact detection within falls remains challenging. This study proposes an efficient methodology for accurately detecting impacts within fall events by incorporating 3D joints skeleton data treated as a graph using Spatio-Temporal Graph Convolutional Networks (STGCN), Gated Recurrent Unit (GRU), and Bidirectional Long Short-Term Memory (BiLSTM) layers. By pinpointing impact moments, our approach enhances precision by distinguishing between false falls and actual impacts, contributing to better healthcare resource allocation. Our methodology, evaluated using the improved 3D skeletons UP-Fall dataset, achieves accuracy exceeding 90\% across various fall scenarios. We have made this improved dataset publicly available at https://zenodo.org/records/12773013 to facilitate further research.
Journal refJournal of Healthcare Informatics Research, vol. 10, no. 1, pp. 209-245, 2026
DOI：10.1007/s41666-025-00215-7
