用床下压力传感器生成图像，提前预测老人离床意图。
ViFusionTST: Deep Fusion of Time-Series Image Representations from Load Signals for Early Bed-Exit Prediction
- 将压力信号转为波形图与三种纹理图，用双流Transformer融合分析。
- 在95张床的真实数据上达到F1 0.794，优于现有时序模型。
- 无需摄像头，隐私友好，适合养老院实时预警场景。
床相关跌倒仍是医院和长期照护机构的主要伤害来源，而许多商用报警器仅在患者离床后才触发。本文展示仅通过安装在床脚下的低成本压力传感器即可提前预测离床意图。原始负载信号被转换为一组紧凑的互补图像：保留原始波形的RGB线图，以及揭示高阶动态的三种纹理图——递归图、马尔可夫转移场和格拉米安角场。我们提出ViFusionTST，一种双流Swin Transformer，平行处理线图与纹理图，并通过交叉注意力融合，学习数据驱动的模态权重。为提供真实基准，我们在一家长期照护机构收集了来自95张床的六个月连续数据。在该真实世界数据集上，ViFusionTST实现0.885的准确率和0.794的F1分数，超越近期一维与二维时序基线，在F1、召回率、准确率和AUPRC上均表现更优。结果表明，基于图像融合的负载信号时序分类是实时、隐私保护的跌倒预防实用方案。
原文摘要 · Abstract (English)
Bed-related falls remain a major source of injury in hospitals and long-term care facilities, yet many commercial alarms trigger only after a patient has already left the bed. We show that early bed-exit intent can be predicted using only one low-cost load cell mounted under a bed leg. The resulting load signals are first converted into a compact set of complementary images: an RGB line plot that preserves raw waveforms and three texture maps-recurrence plot, Markov transition field, and Gramian angular field-that expose higher-order dynamics. We introduce ViFusionTST, a dual-stream Swin Transformer that processes the line plot and texture maps in parallel and fuses them through cross-attention to learn data-driven modality weights. To provide a realistic benchmark, we collected six months of continuous data from 95 beds in a long-term-care facility. On this real-world dataset ViFusionTST reaches an accuracy of 0.885 and an F1 score of 0.794, surpassing recent 1D and 2D time-series baselines across F1, recall, accuracy, and AUPRC. The results demonstrate that image-based fusion of load-sensor signals for time series classification is a practical and effective solution for real-time, privacy-preserving fall prevention.
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