用注意力机制提升蓝牙定位精度，更适合老人活动轨迹识别。
Deep Attention-based Sequential Ensemble Learning for BLE-Based Indoor Localization in Care Facilities
- 将定位任务转为序列学习，融合注意力与双向GRU捕捉移动轨迹。
- 在真实养老院数据上实现0.4438的宏F1，比传统方法高53.1%。
- 适合需要精准追踪老人位置的智能照护系统应用。
养老机构中的室内定位系统可优化人员调配、工作量管理及照护质量。传统基于蓝牙低功耗（BLE）的定位方法将每条时间测量视为独立观测，严重限制性能。本文提出深度注意力序列集成学习（DASEL）框架，将定位重构为序列学习问题。该框架结合频域特征工程、带注意力机制的双向GRU网络、多方向滑动窗口与置信度加权的时间平滑，有效捕捉人类移动轨迹。在真实养老院数据上采用四折时间交叉验证评估，DASEL取得0.4438的宏F1分数，较最优传统基线（0.2898）提升53.1%。
原文摘要 · Abstract (English)
Indoor localization systems in care facilities enable optimization of staff allocation, workload management, and quality of care delivery. Traditional machine learning approaches to Bluetooth Low Energy (BLE)-based localization treat each temporal measurement as an independent observation, fundamentally limiting their performance. To address this limitation, this paper introduces Deep Attention-based Sequential Ensemble Learning (DASEL), a novel framework that reconceptualizes indoor localization as a sequential learning problem. The framework integrates frequency-based feature engineering, bidirectional GRU networks with attention mechanisms, multi-directional sliding windows, and confidence-weighted temporal smoothing to capture human movement trajectories. Evaluated on real-world data from a care facility using 4-fold temporal cross-validation, DASEL achieves a macro F1 score of 0.4438, representing a 53.1% improvement over the best traditional baseline (0.2898).
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