An end-to-end bed-exit prediction system with a single load sensor in real-world care facilities

Timely prediction of a patient’s intention to exit the bed is critical for fall prevention but remains under-explored in real-world care facilities. Existing solutions rely on wearable sensors, cameras, pressure mats, or vibration sensors. However, these existing solutions may raise privacy concerns, incur high hardware costs, require extensive data annotation, and are sometimes noise-sensitive. These systems are typically validated in small laboratory studies or simulated environments, which lack the scale and diversity needed to assess real-world generalization. In this study, we propose a novel bed-exit intention prediction system based on a single load sensor installed under one bed leg and a Transformer-based prediction model, BEDFormer. BEDFormer introduces a Time‑aware De‑stationary Attention mechanism that embeds relative temporal information and dynamically adapts to the non-stationarity of load sensor signals over time. This prediction model can be trained with either annotated or quick heuristic labels for rapid deployment. We collected six months of bed-exit data from 95 beds in a care facility and demonstrated that BEDFormer outperforms recent state‑of‑the‑art time series baselines on all major metrics. Additionally, in a 19‑day field test at a senior care facility, the system achieved an average F1 score of 0.92 across nine residents, confirming strong cross‑site robustness. By turning an inexpensive, privacy‑preserving load sensor into a reliable early‑warning device, BEDFormer lowers the barrier to proactive fall prevention in healthcare settings.