Sistemas de monitoramento de pacientes com IoMT e aprendizado de máquina em cuidados de saúde
Resumo
A Internet das Coisas (IoT) facilita a vigilância e o monitoramento remoto, por meio de redes e protocolos integrados, sendo amplamente utilizada na área da saúde. As instalações médicas estão integrando tecnologias de saúde conectadas para coletar informações dos pacientes, otimizando o diagnóstico, o atendimento e a eficiência operacional. Esta pesquisa apresenta uma estrutura de monitoramento para instalações de serviços médicos, combinando Internet das Coisas Médicas (IoMT) com abordagens de aprendizado de máquina. A solução realiza reconhecimento facial, detecta quedas por meio de padrões de movimento e dispara alertas para cuidadores. Os testes produziram resultados promissores, demonstrando bom desempenho tanto no reconhecimento facial quanto na detecção de quedas.Referências
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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.
Patel, K. K., Patel, S. M., and Scholar, P. (2016). Internet of things-iot: definition, characteristics, architecture, enabling technologies, application & future challenges. International journal of engineering science and computing, 6(5).
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.
Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., and Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2):e1485.
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.
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Alam, E., Sufian, A., Dutta, P., and Leo, M. (2022). Vision-based human fall detection systems using deep learning: A review. Computers in biology and medicine, 146:105626.
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).
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.
Chen, K., Zhang, D., Yao, L., Guo, B., Yu, Z., and Liu, Y. (2021). Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities. ACM Computing Surveys (CSUR), 54(4):1–40.
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).
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.
Kulurkar, P., kumar Dixit, C., Bharathi, V., Monikavishnuvarthini, A., Dhakne, A., and Preethi, P. (2023). Ai based elderly fall prediction system using wearable sensors: A smart home-care technology with iot. Measurement: Sensors, 25:100614.
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.
Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M. G., Lee, J., et al. (2019). Mediapipe: A framework for building perception pipelines. arXiv preprint arXiv:1906.08172.
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.
Patel, K. K., Patel, S. M., and Scholar, P. (2016). Internet of things-iot: definition, characteristics, architecture, enabling technologies, application & future challenges. International journal of engineering science and computing, 6(5).
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.
Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., and Acharya, U. R. (2023). Remote patient monitoring using artificial intelligence: Current state, applications, and challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13(2):e1485.
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.
Visa, S., Ramsay, B., Ralescu, A. L., and Van Der Knaap, E. (2011). Confusion matrix-based feature selection. Maics, 710(1):120–127.
Publicado
20/07/2025
Como Citar
VINICIUS, Marcos; CIANE, Aline; CALLOU, Gustavo; FRANÇA, Cleunio; TAVARES, Eduardo; BEZERRA, Thiago.
Sistemas de monitoramento de pacientes com IoMT e aprendizado de máquina em cuidados de saúde. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 52. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 333-344.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2025.8771.
