Identificação de Situações de Risco para Pacientes em Reabilitação Cardíaca Explorando uma Arquitetura de Software na Internet das Coisas
Abstract
The Internet of Things (IoT) is influencing how computer systems are developed, enabling new application scenarios and a more proactive user interaction, expanding features of mobility and availability. In this work the infrastructure provided by IoT is applied in an architecture to identify risk situations for patients in cardiac rehabilitation. The proposed architecture, called EXEHDA-HP, supports hybrid processing of context data combining techniques based on specification and learning to identify situations.
References
Haghighi, D. P., Averi, P., Indrawan-Santiago, M., and Minh, H. T. (2014). Situationaware mobile health monitoring. ICST (Institute for Computer Sciences, Social- Informatics and Telecommunications Engineering), pages 248—-256.
Kwapisz, J. R., Weiss, G. M., and Moore, S. a. (2011). Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter, 12:74.
Lopes, J., Souza, R., Geyer, C., Costa, C., Barbosa, J., Pernas, A., and Yamin, A. (2014a). A middleware architecture for dynamic adaptation in ubiquitous computing. Journal of Universal Computer Science, 20(9):1327–1351.
Lopes, J., Souza, R., Pernas, A., Yamin, A., and Geyer, C. (2014b). A distributed architecture for supporting context-aware applications in ubicomp. In Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference on, pages 584–590.
Negrão, C. E. and Barreto, A. C. P. (2010). Cardiologia do Exercício: do Atleta ao Cardiopata. Manole, Barueri, SP - Brazil, 3 edition.
Razzaque, M. A., Milojevic-Jevric, M., Palade, A., and Clarke, S. (2015). Middleware for Internet of Things: a Survey. Internet of Things Journal, IEEE, PP(99):1.
Ye, J., Dobson, S., and McKeever, S. (2012). Review: Situation identification techniques in pervasive computing: A review. Pervasive Mob. Comput., 8(1):36–66.
Yuan, B. and Herbert, J. (2014). Context-aware hybrid reasoning framework for pervasive healthcare. Personal and Ubiquitous Computing, 18(4):865–881.
Kwapisz, J. R., Weiss, G. M., and Moore, S. a. (2011). Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter, 12:74.
Lopes, J., Souza, R., Geyer, C., Costa, C., Barbosa, J., Pernas, A., and Yamin, A. (2014a). A middleware architecture for dynamic adaptation in ubiquitous computing. Journal of Universal Computer Science, 20(9):1327–1351.
Lopes, J., Souza, R., Pernas, A., Yamin, A., and Geyer, C. (2014b). A distributed architecture for supporting context-aware applications in ubicomp. In Advanced Information Networking and Applications (AINA), 2014 IEEE 28th International Conference on, pages 584–590.
Negrão, C. E. and Barreto, A. C. P. (2010). Cardiologia do Exercício: do Atleta ao Cardiopata. Manole, Barueri, SP - Brazil, 3 edition.
Razzaque, M. A., Milojevic-Jevric, M., Palade, A., and Clarke, S. (2015). Middleware for Internet of Things: a Survey. Internet of Things Journal, IEEE, PP(99):1.
Ye, J., Dobson, S., and McKeever, S. (2012). Review: Situation identification techniques in pervasive computing: A review. Pervasive Mob. Comput., 8(1):36–66.
Yuan, B. and Herbert, J. (2014). Context-aware hybrid reasoning framework for pervasive healthcare. Personal and Ubiquitous Computing, 18(4):865–881.
Published
2016-07-04
How to Cite
SCHEUNEMANN, Douglas; YAMIN, Adenauer; LOPES, João; GEYER, Cláudio.
Identificação de Situações de Risco para Pacientes em Reabilitação Cardíaca Explorando uma Arquitetura de Software na Internet das Coisas. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTHCARE (SBCAS), 16. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 2621-2624.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2016.9912.
