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Smart Environments and Context-Awareness for Lifestyle Management in a Healthy Active Ageing Framework

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Progress in Artificial Intelligence (EPIA 2015)

Abstract

Health trends of elderly in Europe motivate the need for technological solutions aimed at preventing the main causes of morbidity and premature mortality. In this framework, the DOREMI project addresses three important causes of morbidity and mortality in the elderly by devising an ICT-based home care services for aging people to contrast cognitive decline, sedentariness and unhealthy dietary habits. In this paper, we present the general architecture of DOREMI, focusing on its aspects of human activity recognition and reasoning.

This work has been funded in the framework of the FP7 project “Decrease of cOgnitive decline, malnutRition and sedEntariness by elderly empowerment in lifestyle Management and social Inclusion” (DOREMI), contract N.611650

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References

  1. Tukey, J.W.: Exploratory data analysis, pp. 2–3 (1977)

    Google Scholar 

  2. Long, X., Yin, B., Aarts, R.M.: Single-accelerometer-based daily physical activity classification. In: Engineering in Medicine and Biology Society, EMBC 2009. Annual International Conference of the IEEE. IEEE (2009)

    Google Scholar 

  3. Fernández-Llatas, C., et al.: Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors 13(11), 15434–15451 (2013)

    Article  Google Scholar 

  4. der Aalst, V., Wil, M.P., et al.: Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering 47(2), 237–267 (2003)

    Article  Google Scholar 

  5. Yang, C.-H., Liu, Y.-T., Chuang, L.-Y.: DNA motif discovery based on ant colony optimization and expectation maximization. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, vol. 1 (2011)

    Google Scholar 

  6. Bouamama, S., Boukerram, A., Al-Badarneh, A.F.: Motif finding using ant colony optimization. In: Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 464–471. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Cui, X., et al.: Visual mining intrusion behaviors by using swarm technology. In: 2011 44th Hawaii International Conference on System Sciences (HICSS). IEEE (2011)

    Google Scholar 

  8. Bao, L., Intille, S.S.: Activity Recognition from User-Annotated Acceleration Data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  9. Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. Communications Surveys & Tutorials, IEEE 15(3), 1192–1209 (2013)

    Article  Google Scholar 

  10. Kolen, J., Kremer, S. (eds.): A Field Guide to Dynamical Recurrent Networks. IEEE Press (2001)

    Google Scholar 

  11. Lukoševicius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)

    Article  MATH  Google Scholar 

  12. Jaeger, H., Haas, H.: Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78–80 (2004)

    Article  Google Scholar 

  13. Gallicchio, C., Micheli, A.: Architectural and markovian factors of echo state networks. Neural Networks 24(5), 440–456 (2011)

    Article  Google Scholar 

  14. Tino, P., Hammer, B., Boden, M.: Markovian bias of neural based architectures with feedback connections. In: Hammer, B., Hitzler, P. (eds.) Perspectives of neural-symbolic integration. SCI, vol. 77, pp. 95–133. Springer-Verlag, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Lukoševičius, M., Jaeger, H., Schrauwen, B.: Reservoir Computing Trends. KI - Künstliche Intelligenz 26(4), 365–371 (2012)

    Article  Google Scholar 

  16. Bacciu, D., Barsocchi, P., Chessa, S., Gallicchio, C., Micheli, A.: An experimental characterization of reservoir computing in ambient assisted living applications. Neural Computing and Applications 24(6), 1451–1464 (2014)

    Article  Google Scholar 

  17. Chessa, S., et al.: Robot localization by echo state networks using RSS. In: Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol. 26, pp. 147–154. Springer (2014)

    Google Scholar 

  18. Palumbo, F., Barsocchi, P., Gallicchio, C., Chessa, S., Micheli, A.: Multisensor data fusion for activity recognition based on reservoir computing. In: Botía, J.A., Álvarez-García, J.A., Fujinami, K., Barsocchi, P., Riedel, T. (eds.) EvAAL 2013. CCIS, vol. 386, pp. 24–35. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  19. Bacciu, D., Gallicchio, C., Micheli, A., Di Rocco, M., Saffiotti, A.: Learning context-aware mobile robot navigation in home environments. In: 5th IEEE Int. Conf. on Information, Intelligence, Systems and Applications (IISA) (2014)

    Google Scholar 

  20. Amato, G., Broxvall, M., Chessa, S., Dragone, M., Gennaro, C., López, R., Maguire, L., Mcginnity, T., Micheli, A., Renteria, A., O’Hare, G., Pecora, F.: Robotic UBIquitous COgnitive network. In: Novais, P., Hallenborg, K., Tapia, D.I., Rodrìguez, J.M. (eds.) Ambient Intelligence - Software and Applications. AISC, vol. 153, pp. 191–195. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  21. Lavrac, N., et al.: Intelligent data analysis in medicine. IJCAI 97, 1–13 (1997)

    MathSciNet  Google Scholar 

  22. Chae, Y.M.: Expert Systems in Medicine. In: Liebowitz, J. (ed.) The Handbook of applied expert systems, pp. 32.1–32.20. CRC Press (1998)

    Google Scholar 

  23. Gurgen, F.: Neuronal-Network-based decision making in diagnostic applications. IEEE EMB Magazine 18(4), 89–93 (1999)

    Google Scholar 

  24. Anderson, J.R., Machine learning: An artificial intelligence approach. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) vol. 2. Morgan Kaufmann (1986)

    Google Scholar 

  25. Hastie, T., et al.: The elements of statistical learning, vol. 2(1). Springer (2009)

    Google Scholar 

  26. Murphy, K.P.: Machine learning: a probabilistic perspective. MIT Press (2012)

    Google Scholar 

  27. Carvalho, D.R., Freitas, A.A.: A hybrid decision tree/genetic algorithm method for data mining. Information Sciences 163(1), 13–35 (2004). [EDA1] Tukey, J.W.: Exploratory data analysis, pp. 2–3 (1977)

    Article  Google Scholar 

  28. Fuxreiter, T., et al.: A modular plat- form for event recognition in smart homes. In: 12th IEEE Int. Conf. on e-Health Networking Applications and Services (Healthcom), pp. 1–6 (2010)

    Google Scholar 

  29. Kreiner, K., et al.: Play up! A smart knowledge-based system using games for preventing falls in elderly people. Health Informatics meets eHealth (eHealth 2013). In: Proceedings of the eHealth 2013, OCG, Vienna, pp. 243–248 (2013). ISBN: 978-3-85403-293-9

    Google Scholar 

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Correspondence to Stefano Chessa .

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Bacciu, D. et al. (2015). Smart Environments and Context-Awareness for Lifestyle Management in a Healthy Active Ageing Framework. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-23485-4_6

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