抄録
Indoor location recognition based on beacon signals plays a crucial role in healthcare environments, particularly in nursing homes where nurses are responsible for caring for multiple elderly residents simultaneously. This paper is written as part of the ABC 2026 Challenge (Decode The
Invisible: Activity and Location Recognition Challenge in Care Facility), which focuses on evaluating indoor localization methods under realistic and challenging healthcare conditions. Reliable and accurate localization of nursing staff enables timely assistance, improves care coordination, and enhances the overall efficiency and safety of healthcare workflows. However, real-world beacon data are often noisy, unstable, misaligned, or imbalanced, creating challenges for reliable recognition. This study proposes a supervised learning–based approach for indoor nurse localization with a novel Received Signal Strength Indicator (RSSI) pattern–based preprocessing strategy. Each signal segment is represented using dominant beacon patterns to robustly characterize spatial signal distributions, while an outlier detection mechanism is applied to identify and remove abnormal RSSI patterns caused by environmental dynamics. Based on the refined dataset, a supervised learning model is trained to recognize nurses’ indoor locations in a real healthcare environment. Experimental results demonstrate that the proposed method substantially improves localization performance, achieving 79% accuracy and a 68% F1-score, compared to 58% accuracy and a 34% F1-score obtained using raw beacon signals. These results highlight the effectiveness of RSSI pattern–based preprocessing for robust indoor localization in healthcare settings.
