Deep Attention-based Sequential Ensemble Learning for BLE-Based Indoor Localization in Care Facilities
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918401417412608 |
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| author | Pham, Minh Triet Dang, Quynh Chi Tan, Le Nhat |
| author_facet | Pham, Minh Triet Dang, Quynh Chi Tan, Le Nhat |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21030 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Attention-based Sequential Ensemble Learning for BLE-Based Indoor Localization in Care Facilities Pham, Minh Triet Dang, Quynh Chi Tan, Le Nhat Machine Learning Human-Computer Interaction 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). |
| title | Deep Attention-based Sequential Ensemble Learning for BLE-Based Indoor Localization in Care Facilities |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2603.21030 |