Floor Plan Aware Feature Engineering and Gradient Boosted Learning for BLE Based Indoor Localization

抄録

In nursing care facilities, practical challenges with Bluetooth Low Energy (BLE)-based indoor localization include extreme RSSI noise, temporal sparsity, and class imbalance between rooms. This study does not introduce a new localization algorithm; instead, it proposes a floor plan aware feature engineering framework that transforms raw RSSI signals into spatially structured representations by embedding prior knowledge of beacon placement, room adjacency, and temporal activity patterns. RSSI statistics, zone based aggregation, inter zone interactions, proximity based transformations, and temporal behavioral indicators that represent routine movement dynamics inside the facility are all included into the designed features. XGBoost obtains 73.35% accuracy under stratified evaluation (weighted F1: 0.7282). The accuracy of Leave-One-Day-Out cross validation (LODO-CV), which is used to evaluate resilience against temporal bias, is 54.1% (weighted F1: 0.5230) under day level generalization. While temporal indicators provide moderate increases under rigorous validation, ablation analysis shows that spatial feature groupings contribute cooperatively. The majority of errors, according to confusion matrix analysis, happen between geographically neighboring rooms, indicating coherent spatial learning rather than random signal correlation. These findings show that feature design that incorporates floor plan and behavioral context enhances resilience under practical deployment constraints without the need for more sensors or calibration.