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).

翻译：养老设施中的室内定位系统能够优化人员调配、工作负载管理及护理服务质量。传统基于蓝牙低功耗（BLE）定位的机器学习方法将每次时间测量视为独立观测，从根本上限制了其性能。为突破这一局限，本文提出了基于深度注意力的序列集成学习（DASEL）框架，该框架将室内定位重新定义为序列学习问题。该框架融合了基于频率的特征工程、具有注意力机制的双向GRU网络、多方向滑动窗口以及置信度加权的时间平滑技术，以捕捉人体运动轨迹。在养老设施实测数据上采用4折时间交叉验证进行评估，DASEL的宏F1分数达到0.4438，较最优传统基线（0.2898）提升了53.1%。