Uma Arquitetura Fog-Cloud para o Monitoramento de Sinais Corporais
Resumo
Com a popularização de sensores vestíveis cada vez mais disseminada, a observação de dados corporais se tornou ainda mais pertinente. Por meio do estudo dos dados fornecidos por tais sensores, juntamente com dados de localização, para enriquecer as análises, somos capazes de detectar situações de estresse. Para que tal tarefa seja exequível, é necessária uma arquitetura robusta que comporte o volume de dados a serem consumidos pelos sistemas que detectam tais situações. Dessa forma, este trabalho apresenta uma proposta de arquitetura Fog-Cloud que faz uso de dados corporais e de localização por meio de técnicas de Machine Learning para determinar momentos de estresse do usuário.Referências
Aditya, S. and Tibarewala, D. (2012). Comparing ann, lda, qda, knn and svm algorithms in classifying relaxed and stressful mental state from two-channel prefrontal eeg data. International Journal of Artificial Intelligence and Soft Computing, 3(2):143–164.
Bove, L. A. (2019). Increasing patient engagement through the use of wearable technology. The Journal for Nurse Practitioners, 15(8):535–539.
Ciabattoni, L., Ferracuti, F., Longhi, S., Pepa, L., Romeo, L., and Verdini, F. (2017). Real-time mental stress detection based on smartwatch. In 2017 IEEE International Conference on Consumer Electronics (ICCE), pages 110–111. IEEE.
Gravina, R. and Fortino, G. (2020). Wearable body sensor networks: State-of-the-art and research directions. IEEE Sensors Journal, 21(11):12511–12522.
Kiran, M., Murphy, P., Monga, I., Dugan, J., and Baveja, S. S. (2015). Lambda architecture for cost-effective batch and speed big data processing. In 2015 IEEE International Conference on Big Data (Big Data), pages 2785–2792. IEEE.
Larcher, L., Ströele, V., Dantas, M., and Bauer, M. (2020). Event-driven framework for detecting unusual patterns in aal environments. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pages 309–314. IEEE.
Munir, A., Kansakar, P., and Khan, S. U. (2017). Ifciot: Integrated fog cloud iot: A novel architectural paradigm for the future internet of things. IEEE Consumer Electronics Magazine, 6(3):74–82.
Priya, A., Garg, S., and Tigga, N. P. (2020). Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Computer Science, 167:1258–1267.
Santhanagopalan, M., Chetty, M., Foale, C., Aryal, S., and Klein, B. (2018). Relevance of frequency of heart-rate peaks as indicator of ‘biological’stress level. In International Conference on Neural Information Processing, pages 598–609. Springer.
Wan, J., Al-awlaqi, M. A., Li, M., O’Grady, M., Gu, X., Wang, J., and Cao, N. (2018). Wearable iot enabled real-time health monitoring system. EURASIP Journal on Wireless Communications and Networking, 2018(1):1–10.
Bove, L. A. (2019). Increasing patient engagement through the use of wearable technology. The Journal for Nurse Practitioners, 15(8):535–539.
Ciabattoni, L., Ferracuti, F., Longhi, S., Pepa, L., Romeo, L., and Verdini, F. (2017). Real-time mental stress detection based on smartwatch. In 2017 IEEE International Conference on Consumer Electronics (ICCE), pages 110–111. IEEE.
Gravina, R. and Fortino, G. (2020). Wearable body sensor networks: State-of-the-art and research directions. IEEE Sensors Journal, 21(11):12511–12522.
Kiran, M., Murphy, P., Monga, I., Dugan, J., and Baveja, S. S. (2015). Lambda architecture for cost-effective batch and speed big data processing. In 2015 IEEE International Conference on Big Data (Big Data), pages 2785–2792. IEEE.
Larcher, L., Ströele, V., Dantas, M., and Bauer, M. (2020). Event-driven framework for detecting unusual patterns in aal environments. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pages 309–314. IEEE.
Munir, A., Kansakar, P., and Khan, S. U. (2017). Ifciot: Integrated fog cloud iot: A novel architectural paradigm for the future internet of things. IEEE Consumer Electronics Magazine, 6(3):74–82.
Priya, A., Garg, S., and Tigga, N. P. (2020). Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Computer Science, 167:1258–1267.
Santhanagopalan, M., Chetty, M., Foale, C., Aryal, S., and Klein, B. (2018). Relevance of frequency of heart-rate peaks as indicator of ‘biological’stress level. In International Conference on Neural Information Processing, pages 598–609. Springer.
Wan, J., Al-awlaqi, M. A., Li, M., O’Grady, M., Gu, X., Wang, J., and Cao, N. (2018). Wearable iot enabled real-time health monitoring system. EURASIP Journal on Wireless Communications and Networking, 2018(1):1–10.
Publicado
26/10/2021
Como Citar
LEÃO, Wagno Sérgio; SILVA, Gabriel Di iorio; STRÖELE, Victor; DANTAS, Mário.
Uma Arquitetura Fog-Cloud para o Monitoramento de Sinais Corporais. In: WORKSHOP DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 22. , 2021, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 9-16.
DOI: https://doi.org/10.5753/wscad_estendido.2021.18635.
