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Deep Attention-based Sequential Ensemble Learning for BLE-Based Indoor Localization in Care Facilities

Minh Triet Pham, Quynh Chi Dang, Le Nhat Tan

Abstract

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).

Deep Attention-based Sequential Ensemble Learning for BLE-Based Indoor Localization in Care Facilities

Abstract

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).
Paper Structure (15 sections, 3 equations, 9 figures, 3 tables, 3 algorithms)

This paper contains 15 sections, 3 equations, 9 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Methodology
  3. Dataset
  4. Preprocessing
  5. Model Training
  6. Traditional Machine Learning Baseline
  7. Deep Attention-based Sequential Ensemble Learning (DASEL)
  8. Phase 1: Frequency-Based Feature Engineering
  9. Phase 2: Sequential Model Training
  10. Phase 3: Multi-Level Ensemble Inference
  11. Phase 4: Temporal Smoothing
  12. Evaluation Protocol
  13. Results and Analysis
  14. Discussion
  15. Conclusion

Figures (9)

  • Figure 1: 5th floor map with beacon positions and room layout b20b26.
  • Figure 2: Overall class distribution across all four days.
  • Figure 3: Traditional machine learning workflow.
  • Figure 4: DASEL complete workflow.
  • Figure 5: DASEL model architecture.
  • ...and 4 more figures