Abstract
The use of IoT devices is increasing and their integration into healthcare is growing. Therefore, there is a need to develop microservice-oriented hardware-software architectures that integrate all the stages from the acquisition of physiological signals to their processing and classification. In addition, the integration of physiological signals from different sources is a must in order to increase the knowledge of the monitored person’s condition. In this context, the focus of this work has been to identify all the necessary workflow phases in this type of architecture, focusing mainly on scalability, replication and redundancy of the different services. This work proposes an architecture generic in terms of the number of sensors to be included and their acquisition requirements (sampling frequency and latency). We have chosen to include network protocols such as the Laboratory Stream Layer for data synchronisation and streaming. To this end, infrastructure as a service and as a machine have been included.
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Acknowledgements
The work leading to this paper has received funding from the following sources: REBECCA project funded by HORIZON-KDT with reference 101097224; Grant PCI2022-135043-2 funded by MCIN/AEI/10.13039/501100011033 and by “Next Generation EU/PRTR”; Grants PID2020-115220RB-C21 and EQC2019-006063-P funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way to make Europe”; Grant 2022-GRIN-34436 funded by Universidad de Castilla-La Mancha and by “ERDF A way of making Europe”; Grants PTA2019-016876-I and RYC-2017-22836 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”. This research was also supported by CIBERSAM, Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación.
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Sánchez-Reolid, R., Sánchez-Reolid, D., Ayora, C., de la Vara, J.L., Pereira, A., Fernández-Caballero, A. (2023). Generic Architecture for Multisource Physiological Signal Acquisition, Processing and Classification Based on Microservices. In: Ossowski, S., Sitek, P., Analide, C., Marreiros, G., Chamoso, P., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-031-38333-5_13
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