Uma Abordagem não Intrusiva para Reconhecimento de Atividades em Casas Inteligentes Explorando Processamento Semântico
Resumo
O objetivo deste trabalho, denominado EXEHDA-AR (Execution Environment for Highly Distributed Applications-Activity Recognition), é prover recursos arquiteturais que permitam o reconhecimento de atividades no middleware EXEHDA, explorando uma abordagem baseada em processamento semântico. Para tanto foram concebidos componentes arquiteturais, os quais foram integrados ao Subsistema de Reconhecimento de Contexto e Adaptação do EXEHDA. Um estudo de caso sobre casas inteligentes foi desenvolvido para avaliar as funcionalidades propostas para o EXEHDA-AR, sendo obtida uma acurácia média de 94,36% no reconhecimento de atividades. Estes resultados apontam que métodos baseados em processamento semântico constituem uma alternativa viável, com baixo nível de intrusão.
Referências
Abreu, E. (2017). Reconhecimento de Atividades em Casas Inteligentes: Uma abordagem não Intrusiva explorando Processamento Semântico. Dissertação (mestrado em ciência da computação), Programa de Pós-Graduação em Computação, Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, RS.
Al-Shaqi, R., Mourshed, M., and Rezgui, Y. (2016). Progress in ambient assisted systems for independent living by the elderly. SpringerPlus, 5(1):624.
Chen, L. and Nugent, C. (2009). Ontology-based activity recognition in intelligent pervasive environments. International Journal of Web Information Systems, 5(4):410–430.
Chen, L., Nugent, C., Mulvenna, M., Finlay, D., and Hong, X. (2009). Semantic smart homes: towards knowledge rich assisted living environments. In Intelligent Patient Management, pages 279–296. Springer.
Cook, D. J. (2012). Learning setting-generalized activity models for smart spaces. IEEE intelligent systems, 27(1):32–38.
Culmone, R., Falcioni, M., Giuliodori, P., Merelli, E., Orru, A., Quadrini, M., Ciampolini, P., Grossi, F., and Matrella, G. (2014). Aal domain ontology for event-based human activity recognition. In Mechatronic and Embedded Systems and Applications (MESA), 2014 IEEE/ASME 10th International Conference on, pages 1–6. IEEE.
Lopes, J. L., de Souza, R. S., Geyer, C., da Costa, C., Barbosa, J., Pernas, A. M., and Yamin, A. (2014). A middleware architecture for dynamic adaptation in ubiquitous computing. J. UCS, 20(9):1327–1351.
Ni, Q., García Hernando, A. B., and Pau de la Cruz, I. (2016). A context-aware system infrastructure for monitoring activities of daily living in smart home. Journal of Sensors, 2016.
Okeyo, G., Chen, L.,Wang, H., and Sterritt, R. (2014). Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive and Mobile Computing, 10:155–172.
Perera, C., Member, C. H. L., Jayawardena, S., and Chen, M. (2015). Context-aware computing in the internet of things: A survey on internet of things from industrial market perspective. arXiv preprint arXiv:1502.00164.
Riboni, D. and Bettini, C. (2011). Cosar: hybrid reasoning for context-aware activity recognition. Personal and Ubiquitous Computing, 15(3):271–289.
Riboni, D., Pareschi, L., Radaelli, L., and Bettini, C. (2011). Is ontology-based activity recognition really effective? In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, pages 427–431. IEEE.
Röcker, C., Ziefle, M., and Holzinger, A. (2014). From computer innovation to human integration: current trends and challenges for pervasive healthtechnologies. In Pervasive Health, pages 1–17. Springer.
Salfner, F., Lenk, M., and Malek, M. (2010). A survey of online failure prediction methods. ACM Computing Surveys (CSUR), 42(3):10.
Tapia, E. M., Intille, S. S., and Larson, K. (2004). Activity recognition in the home using simple and ubiquitous sensors. In International Conference on Pervasive Computing, pages 158–175. Springer.
Van Kasteren, T., Noulas, A., Englebienne, G., and Kröse, B. (2008). Accurate activity recognition in a home setting. In Proceedings of the 10th international conference on Ubiquitous computing, pages 1–9. ACM.
van Kasteren, T. L., Alemdar, H., and Ersoy, C. (2011). Effective performance metrics for evaluating activity recognition methods. ARCS 2011.
Wongpatikaseree, K., Ikeda, M., Buranarach, M., Supnithi, T., Lim, A. O., and Tan, Y. (2012). Activity recognition using context-aware infrastructure ontology in smart home domain. In Knowledge, Information and Creativity Support Systems (KICSS), 2012 Seventh International Conference on, pages 50–57. IEEE.
Al-Shaqi, R., Mourshed, M., and Rezgui, Y. (2016). Progress in ambient assisted systems for independent living by the elderly. SpringerPlus, 5(1):624.
Chen, L. and Nugent, C. (2009). Ontology-based activity recognition in intelligent pervasive environments. International Journal of Web Information Systems, 5(4):410–430.
Chen, L., Nugent, C., Mulvenna, M., Finlay, D., and Hong, X. (2009). Semantic smart homes: towards knowledge rich assisted living environments. In Intelligent Patient Management, pages 279–296. Springer.
Cook, D. J. (2012). Learning setting-generalized activity models for smart spaces. IEEE intelligent systems, 27(1):32–38.
Culmone, R., Falcioni, M., Giuliodori, P., Merelli, E., Orru, A., Quadrini, M., Ciampolini, P., Grossi, F., and Matrella, G. (2014). Aal domain ontology for event-based human activity recognition. In Mechatronic and Embedded Systems and Applications (MESA), 2014 IEEE/ASME 10th International Conference on, pages 1–6. IEEE.
Lopes, J. L., de Souza, R. S., Geyer, C., da Costa, C., Barbosa, J., Pernas, A. M., and Yamin, A. (2014). A middleware architecture for dynamic adaptation in ubiquitous computing. J. UCS, 20(9):1327–1351.
Ni, Q., García Hernando, A. B., and Pau de la Cruz, I. (2016). A context-aware system infrastructure for monitoring activities of daily living in smart home. Journal of Sensors, 2016.
Okeyo, G., Chen, L.,Wang, H., and Sterritt, R. (2014). Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive and Mobile Computing, 10:155–172.
Perera, C., Member, C. H. L., Jayawardena, S., and Chen, M. (2015). Context-aware computing in the internet of things: A survey on internet of things from industrial market perspective. arXiv preprint arXiv:1502.00164.
Riboni, D. and Bettini, C. (2011). Cosar: hybrid reasoning for context-aware activity recognition. Personal and Ubiquitous Computing, 15(3):271–289.
Riboni, D., Pareschi, L., Radaelli, L., and Bettini, C. (2011). Is ontology-based activity recognition really effective? In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, pages 427–431. IEEE.
Röcker, C., Ziefle, M., and Holzinger, A. (2014). From computer innovation to human integration: current trends and challenges for pervasive healthtechnologies. In Pervasive Health, pages 1–17. Springer.
Salfner, F., Lenk, M., and Malek, M. (2010). A survey of online failure prediction methods. ACM Computing Surveys (CSUR), 42(3):10.
Tapia, E. M., Intille, S. S., and Larson, K. (2004). Activity recognition in the home using simple and ubiquitous sensors. In International Conference on Pervasive Computing, pages 158–175. Springer.
Van Kasteren, T., Noulas, A., Englebienne, G., and Kröse, B. (2008). Accurate activity recognition in a home setting. In Proceedings of the 10th international conference on Ubiquitous computing, pages 1–9. ACM.
van Kasteren, T. L., Alemdar, H., and Ersoy, C. (2011). Effective performance metrics for evaluating activity recognition methods. ARCS 2011.
Wongpatikaseree, K., Ikeda, M., Buranarach, M., Supnithi, T., Lim, A. O., and Tan, Y. (2012). Activity recognition using context-aware infrastructure ontology in smart home domain. In Knowledge, Information and Creativity Support Systems (KICSS), 2012 Seventh International Conference on, pages 50–57. IEEE.
Publicado
22/07/2018
Como Citar
ABREU, Eduardo; CAVALHEIRO, Gerson; PERNAS, Ana; YAMIN, Adenauer; LOPES, João; GEYER, Cláudio.
Uma Abordagem não Intrusiva para Reconhecimento de Atividades em Casas Inteligentes Explorando Processamento Semântico. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 10. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 121-130.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2018.3294.
