Título:	Body sensor network : a self-adaptive system exemplar in the healthcare domain
Autor(es):	Gil, Eric Bernd Caldas, Ricardo Diniz Rodrigues, Arthur Silva, Gabriel Levi Gomes da Rodrigues, Genaína Nunes Pelliccione, Patrizio
Assunto:	Sensores Assistência médica Sistemas de informação Sistemas operacionais (Computadores) Monitoramento de pacientes Robôs - programação
Data de publicação:	Jun-2021
Editora:	IEEE
Referência:	GIL, Eric Bernd et al. Body sensor network: a self-adaptive system exemplar in the healthcare domain. In: INTERNACIONAL SYMPOSIUM ON SOFTWARE ENGINEERING FOR ADAPTATIVE AND SELF-MANAGING SYSTEMS (SEAMS), 2021, [S.l.]. Proceedings [...]. [S.l]: IEEE Computer Society, 2021. p. 224-230. DOI: http://doi.org/10.1109/SEAMS51251.2021.00037. Disponível em: https://ieeexplore.ieee.org/document/9462020. Acesso em: 09 fev. 2021.
Abstract:	Recent worldwide events shed light on the need of human-centered systems engineering in the healthcare domain. These systems must be prepared to evolve quickly but safely, according to unpredicted environments and ever-changing pathogens that spread ruthlessly. Such scenarios suffocate hospitals' infrastructure and disable healthcare systems that are not prepared to deal with unpredicted environments without costly re-engineering. In the face of these challenges, we offer the SA-BSN - Self-Adaptive Body Sensor Network - prototype to explore the rather dynamic patient's health status monitoring. The exemplar is focused on self-adaptation and comes with scenarios that hinder an interplay between system reliability and battery consumption that is available after each execution. Also, we provide: (i) a noise injection mechanism, (ii) file-based patient profiles' configuration, (iii) six healthcare sensor simulations, and (iv) an extensible/reusable controller implementation for self-adaptation. The artifact is implemented in ROS (Robot Operating System), which embraces principles such as ease of use and relies on an active open source community support.
Unidade Acadêmica:	Instituto de Ciências Exatas (IE) Departamento de Ciência da Computação (IE CIC)
Licença:	© Copyright 2022 IEEE - All rights reserved.
DOI:	http://doi.org/10.1109/SEAMS51251.2021.00037
Versão da editora:	https://ieeexplore.ieee.org/document/9462020
Aparece nas coleções:	Trabalhos apresentados em evento

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