Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning

Indoor localization systems are critical in various domains, particularly healthcare, where real-time monitoring of elderly and dementia patients is essential. Current systems face significant challenges in achieving both high accuracy and real-time performance in indoor environments. To address this issue, this study proposes an accurate and real-time indoor localization system that integrates Bluetooth Low Energy (BLE) beacons, wearable device, and advanced machine learning algorithm to enhance room-level localization accuracy. We explored and optimized six machine learning models, including XGBoost, LightGBM, Random Forest, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). A Bayesian optimization framework, Optuna, was used to optimize the hyperparameters of machine learning models. Received Signal Strength Indicator (RSSI) data from 15 participants across 10 rooms were collected and processed for performance evaluation and comparison. Based on the experimental results, XGBoost emerged as the highest performing model, with an average accuracy, precision, recall, and F1-score of 0.91. The complete system demonstrates real-time capability, with an end-to-end execution time of 1,346.27 ms. This highlights the system's potential for practical, accurate, and real-time indoor localization.