MedTracker: A BLE-Based Indoor Localization System for Tracking Portable Medical Devices in Hospital Environments

• Higor D. Oliveira UFES https://orcid.org/0009-0006-0310-8457
• Elisa M. Sarmento UFES
• Eduardo S. Saleme UFES https://orcid.org/0009-0008-6336-1942
• Luis A. Souza Jr UFES https://orcid.org/0000-0002-7060-6097
• Gustavo C. Vivas UFES
• Daniel R. Trindade IFES https://orcid.org/0009-0000-6264-6390
• Rodolfo S. Villaça UFES https://orcid.org/0000-0002-8051-3978
• André G. C. Pacheco UFES https://orcid.org/0000-0003-0117-9308

DOI: https://doi.org/10.5753/sbrc_estendido.2026.22861

Resumo

The misplacement and inefficient tracking of Portable Medical Devices (PMDs) represent a persistent operational challenge in hospital environments, leading to delays in care delivery and increased burden on medical staff. This paper presents MedTracker, an indoor localization tool based on Bluetooth Low Energy (BLE), designed for real-time asset tracking in healthcare facilities. The system employs BLE tags attached to monitored devices and strategically deployed ESP32-based gateways that forward signal data to a central server. By combining BLE signals and filtering techniques with machine learning classification and clustering algorithms, MedTracker is capable of inferring room- and floor-level device location, as well as detecting location changes over time. The proposed tool was motivated by practical demands observed at a public university hospital and validated through experimental evaluation, demonstrating its effectiveness as a scalable and deployable solution for hospital asset management.

Referências

Al-Maktary, O., Susanto, M., and Mardiana, M. (2025). Evaluation of rssi-based distance estimation with esp32 ble modules for indoor asset tracking. ELKHA: Jurnal Teknik Elektro, 17(2):172–179.

Aziz, T. and Koo, I. (2025). A comprehensive review of indoor localization techniques and applications in various sectors. Applied Sciences, 15(3):1544.

Barach, P., Fisher, S. D., Adams, M. J., Burstein, G. R., Brophy, P. D., Kuo, D. Z., and Lipshultz, S. E. (2020). Disruption of healthcare: Will the covid pandemic worsen non-covid outcomes and disease outbreaks? Progress in Pediatric Cardiology, 59:101254.

ESPresense Project (2024). Espresense: Esp32-based ble presence detection.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830.

Sharma, A., Borah, S. B., and Moses, A. C. (2021). Responses to covid-19: The role of governance, healthcare infrastructure, and learning from past pandemics. Journal of Business Research, 122:597–607.

Zafari, F., Gkelias, A., and Leung, K. (2019). A survey of indoor localization systems and technologies. arxiv. arXiv:1709.01015v3.

Zhou, C., Yuan, J., Liu, H., and Qiu, J. (2017). Bluetooth indoor positioning based on RSSI and kalman filter. Wireless Personal Communications, 96(3):4115–4130.