抄録
Understanding caregiver behavior in nursing facilities requires utilizing real-world sensor data that is often noisy, misaligned, and imbalanced. We address this using a multimodal dataset from the Activity and Location Recognition Challenge in Care Facility, which includes BLE beacon signal collected in a working nursing home. Rather than proposing a new model architecture, we focus on a simple but effective feature representation: the count of distinct BLE beacons detected within each time window. This captures coarse spatial context without relying solely on unstable RSSI values or explicit distance estimation. Using this feature alongside RSSI, we evaluate several tree-based ensemble methods and select XGBoost as our base classifier with highest accuracy of 60% for its robustness under class imbalance. Our results demonstrate that even with minimal preprocessing and no complex fusion mechanisms, location prediction is achievable in realistic, imperfect conditions.
https://ror.org/01qxhf360 
