An approach based on IoT and machine learning for monitoring patients on healthcare centers

• Thiago Bezerra UFPE / IFPE
• Marcos Vinicius IFPE
• Aline Ciane IFPE
• Gustavo Callou UFRPE
• Cleunio França UFPE
• Eduardo Tavares UFPE

DOI: https://doi.org/10.5753/sbcas.2025.7031

Resumo

Internet of Things (IoT) enables remote monitoring using existing networks, integrating diverse computing systems and communication protocols. This adaptability makes IoT applicable across domains, including healthcare. Hospitals leverage the Internet of Medical Things (IoMT) to collect and transmit patient data, enabling real-time diagnoses, operational efficiency, and personalized care. This paper presents a patient monitoring system for healthcare centers using IoMT and machine learning (ML). The system is capable of performing facial recognition of patients, identifying movement patterns associated with falls, using cameras, and sending alerts to caregivers or healthcare professionals. Results demonstrate the system’s feasibility in enhancing healthcare outcomes. The system achieved a precision of 95% for facial recognition and 97% for fall detection.

Referências

Altameem, T. and Altameem, A. (2020). Facial expression recognition using human machine interaction and multi-modal visualization analysis for healthcare applications. Image and Vision Computing, 103:104044.

Askar, N. A., Habbal, A., Mohammed, A. H., Sajat, M. S., Yusupov, Z., and Kodirov, D. (2022). Architecture, protocols, and applications of the internet of medical things (iomt). Journal of Communications, 17(11).

Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., and Sikdar, B. (2021). A review on the role of machine learning in enabling iot based healthcare applications. IEEE Access, 9:38859–38890.

Bisogni, C., Castiglione, A., Hossain, S., Narducci, F., and Umer, S. (2022). Impact of deep learning approaches on facial expression recognition in healthcare industries. IEEE Transactions on Industrial Informatics, 18(8):5619–5627.

Cao, Q., Shen, L., Xie, W., Parkhi, O. M., and Zisserman, A. (2018). Vggface2: A dataset for recognising faces across pose and age. In 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pages 67–74. IEEE.

Cisco, T. and Internet, A. (2020). Cisco: 2020 ciso benchmark report. Comput. Fraud Secur., 2020(3):4–4.

Doan, T.-N. (2022). An efficient patient activity recognition using lstm network and high-fidelity body pose tracking. International Journal of Advanced Computer Science and Applications, 13(8).

Erickson, B. J. and Kitamura, F. (2021). Magician’s corner: 9. performance metrics for machine learning models.

Hireche, R., Mansouri, H., and Pathan, A.-S. K. (2022). Security and privacy management in internet of medical things (iomt): A synthesis. Journal of Cybersecurity and Privacy, 2(3):640–661.

Koçak, H. and Çetin, G. (2021). A deep learning-based iot implementation for detection of patients’ falls in hospitals. In Trends in Data Engineering Methods for Intelligent Systems: Proceedings of the International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2020), pages 465–483. Springer.

Kwolek, B. and Kepski, M. (2014). Human fall detection on embedded platform using depth maps and wireless accelerometer. Computer methods and programs in biomedicine, 117(3):489–501.

Masud, M., Muhammad, G., Alhumyani, H., Alshamrani, S. S., Cheikhrouhou, O., Ibrahim, S., and Hossain, M. S. (2020). Deep learning-based intelligent face recognition in iot-cloud environment. Computer Communications, 152:215–222.

Mitchell, T. M. and Mitchell, T. M. (1997). Machine learning, volume 1. McGraw-hill New York.

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.

Rajan Jeyaraj, P. and Nadar, E. R. S. (2022). Smart-monitor: Patient monitoring system for iot-based healthcare system using deep learning. IETE Journal of Research, 68(2):1435–1442.

Talaat, F. M. (2023). Real-time facial emotion recognition system among children with autism based on deep learning and iot. Neural Computing and Applications, 35(17):12717–12728.

Verma, D., Singh, K. R., Yadav, A. K., Nayak, V., Singh, J., Solanki, P. R., and Singh, R. P. (2022). Internet of things (iot) in nano-integrated wearable biosensor devices for healthcare applications. Biosensors and Bioelectronics: X, 11:100153.

Visa, S., Ramsay, B., Ralescu, A. L., and Van Der Knaap, E. (2011). Confusion matrix-based feature selection. Maics, 710(1):120–127.

Vishnu, S., Ramson, S. J., and Jegan, R. (2020). Internet of medical things (iomt)- an overview. In 2020 5th international conference on devices, circuits and systems (ICDCS), pages 101–104. IEEE.