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Acquisition and Synchronisation of Multi-source Physiological Data Using Microservices and Event-Driven Architecture

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Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence (ISAmI 2022)

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Abstract

This paper introduces an architecture based on microservices and events for the acquisition and synchronisation of physiological signals. The proposed architecture incorporates the biosensors and a gateway that synchronises, secures and connects the traffic with the back-end infrastructure. This infrastructure is in turn composed of numerous services that are responsible for acquiring, storing and classifying the synchronised data. It has been chosen to integrate two non-invasive devices in a first version of the architecture. These are an electrocardiogram sensor and a wearable device that monitors electrodermal activity, blood volume pressure, time between heartbeats, skin surface temperature and acceleration. Tests have shown that the proposed approach improves synchronisation times using the edge compared to direct data delivery to the cloud.

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References

  1. Al-Asadi, A.M.Q.K., Muttair, K.S., Wadday, A.G., Mosleh, M.F.: Wireless body-area network monitoring with ZigBee, 5G and 5G with MIMO for outdoor environments. Bull. Electrical Eng. Inf. 11(2), 893–900 (2022)

    Google Scholar 

  2. Cai, Y., Genovese, A., Piuri, V., Scotti, F., Siegel, M.: IoT-based architectures for sensing and local data processing in ambient intelligence: research and industrial trends. In: 2019 IEEE International Instrumentation and Measurement Technology Conference, pp. 1–6. IEEE (2019)

    Google Scholar 

  3. Castillo, J.C., Fernández-Caballero, A., Castro-González, Á., Salichs, M.A., López, M.T.: A framework for recognizing and regulating emotions in the elderly. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) Ambient Assisted Living and Daily Activities, pp. 320–327. Springer International Publishing, Cham (2014)

    Chapter  Google Scholar 

  4. Del-Valle-Soto, C., Valdivia, L.J., Velazquez, R., Visconti, P.: Remotely vital signs capturer for older adults applied in residential zones. In: IEEE Consumer Electronics Magazine (2022)

    Google Scholar 

  5. Dilmaghani, R.S., Bobarshad, H., Ghavami, M., Choobkar, S., Wolfe, C.: Wireless sensor networks for monitoring physiological signals of multiple patients. IEEE Trans. Biomed. Circuits Syst. 5(4), 347–356 (2011)

    Article  Google Scholar 

  6. Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., Schreier, G.: The internet of things for ambient assisted living. In: 2010 Seventh International Conference on Information Technology, pp. 804–809. IEEE (2010)

    Google Scholar 

  7. Guerrero, G., da Silva, F.J.M., Fernández-Caballero, A., Pereira, A.: Augmented humanity: a systematic mapping review. Sensors 22(2), 514 (2022)

    Article  Google Scholar 

  8. Lozano-Monasor, E., López, M.T., Vigo-Bustos, F., Fernández-Caballero, A.: Facial expression recognition in ageing adults: from lab to ambient assisted living. J. Ambient Intell. Humanized Comput. 8(4), 567–578 (2017). https://doi.org/10.1007/s12652-017-0464-x

    Article  Google Scholar 

  9. Lozano-Monasor, E., López, M.T., Fernández-Caballero, A., Vigo-Bustos, F.: Facial expression recognition from webcam based on active shape models and support vector machines. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) Ambient Assisted Living and Daily Activities, pp. 147–154. Springer International Publishing, Cham (2014)

    Chapter  Google Scholar 

  10. Martínez-Rodrigo, A., García-Martínez, B., Alcaraz, R., González, P., Fernández-Caballero, A.: Multiscale entropy analysis for recognition of visually elicited negative stress from eeg recordings. Int. J. Neural Syst. 29(2), 1850038 (2019)

    Article  Google Scholar 

  11. Merkel, D.: Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014)

    Google Scholar 

  12. Mokhtar, S.B., Liu, J., Georgantas, N., Issarny, V.: QoS-aware dynamic service composition in ambient intelligence environments. In: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering, pp. 317–320 (2005)

    Google Scholar 

  13. Nakashima, H., Aghajan, H., Augusto, J.C.: Handbook of Ambient Intelligence and Smart Environments. Springer Science & Business Media (2009)

    Google Scholar 

  14. Phan, D.T., Nguyen, C.H., Nguyen, T.D.P., Tran, L.H., Park, S., Choi, J., Lee, B.i., Oh, J.: A flexible, wearable, and wireless biosensor patch with internet of medical things applications. Biosensors 12(3), 139 (2022)

    Google Scholar 

  15. Soni, G., Selvaradjou, K.: Optimal GTS distribution to heterogeneous sensors in IEEE 802.15. 4 network for healthcare monitoring applications. Personal and Ubiquitous Computing 26(1), 131–153 (2022)

    Google Scholar 

  16. Surantha, N., Utomo, O.K., Lionel, E.M., Gozali, I.D., Isa, S.M.: Intelligent sleep monitoring system based on microservices and event-driven architecture. IEEE Access (2022)

    Google Scholar 

  17. Taylor, H., Yochem, A., Phillips, L., Martinez, F.: Event-Driven Architecture: How SOA Enables the Real-time Enterprise. Pearson Education (2009)

    Google Scholar 

  18. Thönes, J.: Microservices. IEEE Softw. 32(1), 116–116 (2015)

    Article  Google Scholar 

  19. Zangróniz, R., Martínez-Rodrigo, A., López, M.T., Pastor, J.M., Fernández-Caballero, A.: Estimation of mental distress from photoplethysmography. Appl. Sci. 8(1), 69 (2018)

    Article  Google Scholar 

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Acknowledgements

Grants PID2020-115220RB-C21 and EQC2019-006063-P funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way to make Europe”. Grants BES-2017-081958 and PTA2019-016876-I funded by MCIN/AEI/10.13039/501100011 033 and by “ESF Investing in your future‘’. This work was also partially supported by Portuguese Fundação para a Ciência e a Tecnologia - FCT, I.P. under project UIDB/04524/2020 and by Portuguese National funds through FITEC - Programa Interface, with reference CIT “INOV - INESC Inovação - Financiamento Base“. This work was also partially supported by CIBERSAM of the Instituto de Salud Carlos III.

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Correspondence to Antonio Fernández-Caballero .

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Sánchez-Reolid, R., Sánchez-Reolid, D., Pereira, A., Fernández-Caballero, A. (2023). Acquisition and Synchronisation of Multi-source Physiological Data Using Microservices and Event-Driven Architecture. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_2

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