Abstract
Indoor location recognition in nursing care facilities is critical for activity monitoring and safety management. Bluetooth Low Energy (BLE) beacon signals collected in real-world environments are noisy and highly imbalanced across locations. But these noises and imbalances make reliable localization challenging. This study aims to develop and evaluate a robust BLE-based indoor location recognition framework that effectively captures short-term temporal signal patterns under realistic care-facility conditions. An established Temporal Convolutional Network (TCN) architecture is applied as part of a TCN-based framework. BLE RSSI signals are provided by the ABC 2026 Activity and Location Recognition Challenge (ALRC). The approach integrates RSSI aggregation, normalization, and masking of missing beacon observations. Then, a fixed-length temporal windowing is used to model temporal dependencies in BLE sequences. Model performance is assessed using a stratified training–validation split, with Macro F1-score adopted as the primary metric to address severe class imbalance. Experimental results demonstrate that the proposed framework achieves a validation accuracy of 0.66 and a Macro F1-score of 0.56 across 22 indoor location classes. Comparative evaluation against five baseline classifiers shows more balanced performance. Particularly, our study has improved recognition of underrepresented locations. The main contribution of this work lies in the adaptation and evaluation of temporal convolutional modeling for BLE-based indoor localization in nursing care environments. Overall, the results indicate that temporal modeling significantly enhances robustness and reliability of BLE-based indoor location recognition in care-facility scenarios.
