O Futuro da Saúde Preditiva: Uma Análise do Estado da Arte em IoT e Sistemas Embarcados para Identificar Oportunidades de Inovação
Resumo
Este trabalho apresenta um guia estratégico para a inovação em IoT na área da saúde, com foco em sistemas embarcados. A pesquisa analisou o crescimento exponencial de publicações até 2024, com destaque para a liderança da Índia e Arábia Saudita, e o destaque no monitoramento de sinais restritos e sistemas de casas inteligentes. O objetivo é identificar falhas e direcionar o desenvolvimento de soluções de saúde proativas e preditivas.
Palavras-chave:
IoT, Sistemas Embarcados, Saúde Preditiva, Inovação, Mapeamento da Literatura
Referências
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Almujally, N. A., Aljrees, T., Saidani, O., Umer, M., Faheem, Z. B., Abuzinadah, N., Alnowaiser, K., and Ashraf, I. (2023). Monitoring acute heart failure patients using internet-of-things-based smart monitoring system. Sensors, 23(10):4580.
Arunachalam, R., Sunitha, G., Shukla, S. K., Pandey, S. N., Urooj, S., and Rawat, S. (2023). A smart Alzheimer’s patient monitoring system with IoT-assisted technology through enhanced deep learning approach. Knowledge and Information Systems, 65(12):5561–5599.
Asif, R. N., Abbas, S., Khan, M. A., Sultan, K., Mahmud, M., and Mosavi, A. (2022). Development and validation of embedded device for electrocardiogram arrhythmia empowered with transfer learning. Computational Intelligence and Neuroscience, 2022(1):5054641.
Astillo, P. V., Duguma, D. G., Park, H., Kim, J., Kim, B., and You, I. (2022). Federated intelligence of anomaly detection agent in IoTMD-enabled Diabetes Management Control System. Future Generation Computer Systems, 128:395–405.
Escobedo, P., Pousibet-Garrido, A., López-Ruiz, N., Carvajal, M. A., Palma, A. J., and Martínez-Olmos, A. (2024). Bed-based ballistocardiography system using flexible RFID sensors for noninvasive single-and dual-subject vital signs monitoring. IEEE Transactions on Instrumentation and Measurement, 73:1–12.
Jain, P., Joshi, A. M., Mohanty, S. P., and Cenkeramaddi, L. R. (2024). Non-invasive glucose measurement technologies: Recent advancements and future challenges. IEEE Access, 12:61907–61936.
Javed, A. R., Fahad, L. G., Farhan, A. A., Abbas, S., Srivastava, G., Parizi, R. M., and Khan, M. S. (2021). Automated cognitive health assessment in smart homes using machine learning. Sustainable Cities and Society, 65:102572.
Kaur, R. and Sharma, R. (2024). Wearable sensors and datasets for evaluating systems predicting falls and activities of daily living: recent advances and methodology. Multimedia Tools and Applications, 83(29):73671–73701.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE-2007-01, School of Computer Science and Mathematics, Keele University and Department of Computer Science, University of Durham, Keele, UK and Durham, UK.
Kumar, R. H. and Rajaram, B. (2024). Design and simulation of an edge compute architecture for IoT-based clinical decision support system. IEEE Access, 12:45456–45474.
Kumar, V. S. and Krishnamoorthi, C. (2021). Development of electrical transduction based wearable tactile sensors for human vital signs monitor: Fundamentals, methodologies and applications. Sensors and Actuators A: Physical, 321:112582.
Manogaran, G., Alazab, M., Song, H., and Kumar, N. (2021). CDP-UA: Cognitive data processing method wearable sensor data uncertainty analysis in the internet of things assisted smart medical healthcare systems. IEEE Journal of Biomedical and Health Informatics, 25(10):3691–3699.
Nekui, O. D., Wang, W., Liu, C., Wang, Z., and Ding, B. (2024). IoT-based heartbeat rate-monitoring device powered by harvested kinetic energy. Sensors, 24(13):4249.
Prasad, P., Hussain, S. A., Thotakura, P., and Sanki, P. K. (2025). Design and Development of an IoT-Based Embedded System for Continuous Monitoring of Vital Signs. Journal of Electronic Materials, 54(5):3444–3451.
Qian, Z., Lin, Y., Jing, W., Ma, Z., Liu, H., Yin, R., Li, Z., Bi, Z., and Zhang, W. (2022). Development of a real-time wearable fall detection system in the context of Internet of Things. IEEE internet of things journal, 9(21):21999–22007.
Rivadeneira, J. E., Fernandes, J. M., Rodrigues, A., Boavida, F., and Silva, J. S. (2024). An Evaluation of Unobtrusive Sensing in a Healthcare Case Study. IEEE Access, 12:89405–89417.
Shoukat, M. U., Yan, L., Zhang, J., Cheng, Y., Raza, M. U., and Niaz, A. (2024). Smart home for enhanced healthcare: exploring human machine interface oriented digital twin model. Multimedia Tools and Applications, 83(11):31297–31315.
Siam, A. I., Almaiah, M. A., Al-Zahrani, A., Elazm, A. A., El Banby, G. M., El-Shafai, W., El-Samie, F. E. A., and El-Bahnasawy, N. A. (2021). Secure health monitoring communication systems based on IoT and cloud computing for medical emergency applications. Computational Intelligence and Neuroscience, 2021(1):8016525.
Sravanthi, M., Gunturi, S. K., Chinnaiah, M. C., Lam, S.-K., Vani, G. D., Basha, M., Janardhan, N., Krishna, D. H., and Dubey, S. (2024). Adaptive FPGA-Based Accelerators for Human–Robot Interaction in Indoor Environments. Sensors (Basel, Switzerland), 24(21):6986.
Tahir, S. B. u. d., Dogar, A. B., Fatima, R., Yasin, A., Shafiq, M., Khan, J. A., Assam, M., Mohamed, A., and Attia, E.-A. (2022). Stochastic recognition of human physical activities via augmented feature descriptors and random forest model. Sensors, 22(17):6632.
Umar, M. A., Abuali, N., Shuaib, K., and Awad, A. I. (2025). An explainable artificial intelligence and Internet of Things framework for monitoring and predicting cardiovascular disease. Engineering Applications of Artificial Intelligence, 144:110138.
Vaiyapuri, T., Alharbi, G., Dharmarajlu, S. M., Bouteraa, Y., Misra, S., Ramesh, J. V. N., and Mohanty, S. N. (2024). IoT-enabled early detection of diabetes diseases using deep learning and dimensionality reduction techniques. IEEE Access.
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.
Xu, W., Hong, L., Zheng, J., Li, M., Hua, Y., and Zhao, X. (2023). Wearable smart sensor system for monitoring and intelligent prediction of sodium ions in human perspiration. IEEE Internet of Things Journal, 11(5):8146–8155.
Yadav, K., Alharbi, A., Jain, A., and Ramadan, R. A. (2022). An IoT based secure patient health monitoring system. Computers, Materials and Continua, 70(2):3637–3652.
Yang, L. (2023). Data monitoring for a physical health system of elderly people using smart sensing technology. Wireless Networks, 29(8):3665–3678.
Yashudas, A., Gupta, D., Prashant, G., Dua, A., AlQahtani, D., and Reddy, A. S. K. (2024). Deep-cardio: Recommendation system for cardiovascular disease prediction using iot network. IEEE Sensors Journal, 24(9):14539–14547.
Zhang, D., Liu, X., Xia, J., Gao, Z., Zhang, H., and de Albuquerque, V. H. C. (2023). A physics-guided deep learning approach for functional assessment of cardiovascular disease in IoT-based smart health. IEEE Internet of Things Journal, 10(21):18505–18516.
Al-Rakhami, M. S., Gumaei, A., Altaf, M., Hassan, M. M., Alkhamees, B. F., Muhammad, K., and Fortino, G. (2021). FallDeF5: A fall detection framework using 5G-based deep gated recurrent unit networks. IEEE Access, 9:94299–94308.
Alkanhel, R. I., Saleh, H., Elaraby, A., Alharbi, S., Elmannai, H., Alaklabi, S., Alsamhi, S. H., and Mostafa, S. (2024). Hybrid cnn-gru model for real-time blood glucose forecasting: Enhancing iot-based diabetes management with ai. Sensors, 24(23):7670.
Almujally, N. A., Aljrees, T., Saidani, O., Umer, M., Faheem, Z. B., Abuzinadah, N., Alnowaiser, K., and Ashraf, I. (2023). Monitoring acute heart failure patients using internet-of-things-based smart monitoring system. Sensors, 23(10):4580.
Arunachalam, R., Sunitha, G., Shukla, S. K., Pandey, S. N., Urooj, S., and Rawat, S. (2023). A smart Alzheimer’s patient monitoring system with IoT-assisted technology through enhanced deep learning approach. Knowledge and Information Systems, 65(12):5561–5599.
Asif, R. N., Abbas, S., Khan, M. A., Sultan, K., Mahmud, M., and Mosavi, A. (2022). Development and validation of embedded device for electrocardiogram arrhythmia empowered with transfer learning. Computational Intelligence and Neuroscience, 2022(1):5054641.
Astillo, P. V., Duguma, D. G., Park, H., Kim, J., Kim, B., and You, I. (2022). Federated intelligence of anomaly detection agent in IoTMD-enabled Diabetes Management Control System. Future Generation Computer Systems, 128:395–405.
Escobedo, P., Pousibet-Garrido, A., López-Ruiz, N., Carvajal, M. A., Palma, A. J., and Martínez-Olmos, A. (2024). Bed-based ballistocardiography system using flexible RFID sensors for noninvasive single-and dual-subject vital signs monitoring. IEEE Transactions on Instrumentation and Measurement, 73:1–12.
Jain, P., Joshi, A. M., Mohanty, S. P., and Cenkeramaddi, L. R. (2024). Non-invasive glucose measurement technologies: Recent advancements and future challenges. IEEE Access, 12:61907–61936.
Javed, A. R., Fahad, L. G., Farhan, A. A., Abbas, S., Srivastava, G., Parizi, R. M., and Khan, M. S. (2021). Automated cognitive health assessment in smart homes using machine learning. Sustainable Cities and Society, 65:102572.
Kaur, R. and Sharma, R. (2024). Wearable sensors and datasets for evaluating systems predicting falls and activities of daily living: recent advances and methodology. Multimedia Tools and Applications, 83(29):73671–73701.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE-2007-01, School of Computer Science and Mathematics, Keele University and Department of Computer Science, University of Durham, Keele, UK and Durham, UK.
Kumar, R. H. and Rajaram, B. (2024). Design and simulation of an edge compute architecture for IoT-based clinical decision support system. IEEE Access, 12:45456–45474.
Kumar, V. S. and Krishnamoorthi, C. (2021). Development of electrical transduction based wearable tactile sensors for human vital signs monitor: Fundamentals, methodologies and applications. Sensors and Actuators A: Physical, 321:112582.
Manogaran, G., Alazab, M., Song, H., and Kumar, N. (2021). CDP-UA: Cognitive data processing method wearable sensor data uncertainty analysis in the internet of things assisted smart medical healthcare systems. IEEE Journal of Biomedical and Health Informatics, 25(10):3691–3699.
Nekui, O. D., Wang, W., Liu, C., Wang, Z., and Ding, B. (2024). IoT-based heartbeat rate-monitoring device powered by harvested kinetic energy. Sensors, 24(13):4249.
Prasad, P., Hussain, S. A., Thotakura, P., and Sanki, P. K. (2025). Design and Development of an IoT-Based Embedded System for Continuous Monitoring of Vital Signs. Journal of Electronic Materials, 54(5):3444–3451.
Qian, Z., Lin, Y., Jing, W., Ma, Z., Liu, H., Yin, R., Li, Z., Bi, Z., and Zhang, W. (2022). Development of a real-time wearable fall detection system in the context of Internet of Things. IEEE internet of things journal, 9(21):21999–22007.
Rivadeneira, J. E., Fernandes, J. M., Rodrigues, A., Boavida, F., and Silva, J. S. (2024). An Evaluation of Unobtrusive Sensing in a Healthcare Case Study. IEEE Access, 12:89405–89417.
Shoukat, M. U., Yan, L., Zhang, J., Cheng, Y., Raza, M. U., and Niaz, A. (2024). Smart home for enhanced healthcare: exploring human machine interface oriented digital twin model. Multimedia Tools and Applications, 83(11):31297–31315.
Siam, A. I., Almaiah, M. A., Al-Zahrani, A., Elazm, A. A., El Banby, G. M., El-Shafai, W., El-Samie, F. E. A., and El-Bahnasawy, N. A. (2021). Secure health monitoring communication systems based on IoT and cloud computing for medical emergency applications. Computational Intelligence and Neuroscience, 2021(1):8016525.
Sravanthi, M., Gunturi, S. K., Chinnaiah, M. C., Lam, S.-K., Vani, G. D., Basha, M., Janardhan, N., Krishna, D. H., and Dubey, S. (2024). Adaptive FPGA-Based Accelerators for Human–Robot Interaction in Indoor Environments. Sensors (Basel, Switzerland), 24(21):6986.
Tahir, S. B. u. d., Dogar, A. B., Fatima, R., Yasin, A., Shafiq, M., Khan, J. A., Assam, M., Mohamed, A., and Attia, E.-A. (2022). Stochastic recognition of human physical activities via augmented feature descriptors and random forest model. Sensors, 22(17):6632.
Umar, M. A., Abuali, N., Shuaib, K., and Awad, A. I. (2025). An explainable artificial intelligence and Internet of Things framework for monitoring and predicting cardiovascular disease. Engineering Applications of Artificial Intelligence, 144:110138.
Vaiyapuri, T., Alharbi, G., Dharmarajlu, S. M., Bouteraa, Y., Misra, S., Ramesh, J. V. N., and Mohanty, S. N. (2024). IoT-enabled early detection of diabetes diseases using deep learning and dimensionality reduction techniques. IEEE Access.
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.
Xu, W., Hong, L., Zheng, J., Li, M., Hua, Y., and Zhao, X. (2023). Wearable smart sensor system for monitoring and intelligent prediction of sodium ions in human perspiration. IEEE Internet of Things Journal, 11(5):8146–8155.
Yadav, K., Alharbi, A., Jain, A., and Ramadan, R. A. (2022). An IoT based secure patient health monitoring system. Computers, Materials and Continua, 70(2):3637–3652.
Yang, L. (2023). Data monitoring for a physical health system of elderly people using smart sensing technology. Wireless Networks, 29(8):3665–3678.
Yashudas, A., Gupta, D., Prashant, G., Dua, A., AlQahtani, D., and Reddy, A. S. K. (2024). Deep-cardio: Recommendation system for cardiovascular disease prediction using iot network. IEEE Sensors Journal, 24(9):14539–14547.
Zhang, D., Liu, X., Xia, J., Gao, Z., Zhang, H., and de Albuquerque, V. H. C. (2023). A physics-guided deep learning approach for functional assessment of cardiovascular disease in IoT-based smart health. IEEE Internet of Things Journal, 10(21):18505–18516.
Publicado
04/12/2025
Como Citar
RIBEIRO, Jhonatas Gomes; SOUZA, Keyllane Francisca Guedes de; LUSTOSA, Phaola Paraguai da Paixão; SILVA, Wanderson Jean Conceição; SANTOS, Felipe Gonçalves dos.
O Futuro da Saúde Preditiva: Uma Análise do Estado da Arte em IoT e Sistemas Embarcados para Identificar Oportunidades de Inovação. In: ESCOLA REGIONAL DE INFORMÁTICA DE GOIÁS (ERI-GO), 13. , 2025, Luziânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 11-20.
DOI: https://doi.org/10.5753/erigo.2025.16973.
