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Background: Bluetooth low energy (BLE) beacons have been used to track the locations of individuals in indoor environments for clinical applications such as workflow analysis and infectious disease modelling. Most current approaches use the received signal strength indicator (RSSI) to track locations. When using the RSSI to track indoor locations, devices need to be calibrated to account for complex interference patterns, which is a laborious process. Our aim was to investigate an alternative method for indoor location tracking of a moving user using BLE beacons in dynamic indoor environments. Methods and Materials: We developed a new method based on the received number of signals indicator (RNSI) and compared it to a standard RSSI-based method for predicting a user's location. Experiments were performed in an office environment and a tertiary hospital. Both RNSI and RSSI were compared at various distances from BLE beacons. In moving user experiments, a user wearing a beacon walked from one location to another based on a pre-defined route. Performance in predicting user locations was measured based on accuracy. Results: RNSI values decreased substantially with distance from the BLE beacon than RSSI values. Moving user experiments in the office environment demonstrated that the RNSI-based method produced higher accuracy (80.0%) than the RSSI-based method (76.2%). In the hospital, where the environment may introduce signal quality problems due to increased signal interference, the RNSI-based method still outperformed (83.3%) the RSSI-based method (51.9%). Conclusions: Our results suggest that the RNSI-based method could be useful to track the locations of a moving user without involving complex calibration, especially when deploying within a new environment. RNSI has the potential to be used together with other methods in more robust indoor positioning systems.
Surian, D., Kim, V., Menon, R., Dunn, A. G., Sintchenko, V., & Coiera, E. (2019). Tracking a moving user in indoor environments using Bluetooth low energy beacons. Journal of Biomedical Informatics, 98. https://doi.org/10.1016/j.jbi.2019.103288