Abstract
Indoor localization in nursing homes, addressed through the ABC 2026 'Decode the Invisible' Challenge, faces significant obstacles because of a strong class imbalance, where minority rooms are underrepresented in the training data and high-traffic areas predominate, leading to inadequate location coverage and poor recognition performance. The study suggests an Ultimate inference framework that combines ensemble voting, minority boosting, and Random Forest classification to achieve 100\% prediction coverage while preserving accuracy across all room classes. The technique processes 4,107 CSV files into 40-second intervals using 33-dimensional feature vectors using Bluetooth Low Energy (BLE) beacon signals from 25 stationary transmitters. The scarcity of minority classes is addressed by augmenting data using Gaussian noise injection ($\sigma = 1.0$ dB). The suggested method successfully recovers (1) rooms 503 and 510 from zero recall to 0.800 and 1.000 F1-scores, respectively; (2) detects 20 out of 22 room classes compared to 18 in baseline methods; and (3) achieves a weighted F1-score of 0.7492 and Macro F1-score of 0.6241 with full coverage. While conventional approaches sacrifice coverage for accuracy, our ensemble-based minority recovery approach maintains macro-level fairness without compromising majority class performance. For healthcare settings with limited resources, this provides a consistent solution.
