A Distributed Framework for Remote Multimodal Biosignal Acquisition and Analysis
Álvarez Casado, Constantino; Räsänen, Pauli; Nguyen, Le Ngu; Lämsä, Arttu; Peltola, Johannes; Bordallo López, Miguel (2024-05-05)
Springer
05.05.2024
Álvarez Casado, C., Räsänen, P., Nguyen, L.N., Lämsä, A., Peltola, J., Bordallo López, M. (2024). A Distributed Framework for Remote Multimodal Biosignal Acquisition and Analysis. In: Särestöniemi, M., et al. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science, vol 2084. Springer, Cham. https://doi.org/10.1007/978-3-031-59091-7_9
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
https://creativecommons.org/licenses/by/4.0/
© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405304106
https://urn.fi/URN:NBN:fi:oulu-202405304106
Tiivistelmä
Abstract
In recent times, several studies have presented single-modality systems for non-contact biosignal monitoring. While these systems often yield estimations correlating with clinical-grade devices, their practicality is limited due to constraints in real-time processing, scalability, and interoperability. Moreover, these studies have seldom explored the combined use of multiple modalities or the integration of various sensors. Addressing these gaps, we introduce a distributed computing architecture designed to remotely acquire biosignals from both radars and cameras. This architecture is supported by conceptual blocks that distribute tasks across sensing, computing, data management, analysis, communication, and visualization. Emphasizing interoperability, our system leverages RESTful APIs, efficient video streaming, and standardized health-data protocols. Our framework facilitates the integration of additional sensors and improves signal analysis efficiency. While the architecture is conceptual, its feasibility has been evaluated through simulations targeting specific challenges in networked remote photoplethysmography (rPPG) systems. Additionally, we implemented a prototype to demonstrate the architectural principles in action, with modules and blocks operating in independent threads. This prototype specifically involves the analysis of biosignals using mmWave radars and RGB cameras, illustrating the potential for the architecture to be adapted into a fully distributed system for real-time biosignal processing.
In recent times, several studies have presented single-modality systems for non-contact biosignal monitoring. While these systems often yield estimations correlating with clinical-grade devices, their practicality is limited due to constraints in real-time processing, scalability, and interoperability. Moreover, these studies have seldom explored the combined use of multiple modalities or the integration of various sensors. Addressing these gaps, we introduce a distributed computing architecture designed to remotely acquire biosignals from both radars and cameras. This architecture is supported by conceptual blocks that distribute tasks across sensing, computing, data management, analysis, communication, and visualization. Emphasizing interoperability, our system leverages RESTful APIs, efficient video streaming, and standardized health-data protocols. Our framework facilitates the integration of additional sensors and improves signal analysis efficiency. While the architecture is conceptual, its feasibility has been evaluated through simulations targeting specific challenges in networked remote photoplethysmography (rPPG) systems. Additionally, we implemented a prototype to demonstrate the architectural principles in action, with modules and blocks operating in independent threads. This prototype specifically involves the analysis of biosignals using mmWave radars and RGB cameras, illustrating the potential for the architecture to be adapted into a fully distributed system for real-time biosignal processing.
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- Avoin saatavuus [38606]
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