Advertisement

A Data Fusion Model for Ambient Assisted Living

  • Javier Jiménez Alemán
  • Nayat Sánchez-Pi
  • Luis Marti
  • José Manuel Molina
  • Ana Cristina Bicharra Garcia
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 616)

Abstract

Ambient Assisted Living (AAL) is an emergent area that provides useful mechanisms that allows tracking elders through sensoring. For AAL systems, it is very important to provide information fusion techniques, which merge the information available in sensors available in different devices like the smartphones to infer possible risk situations for elders in outdoor environments. The Data Fusion Model is the most widely used method for categorizing data fusion-related functions. In previous works we have developed SafeRoute, an AAL system that pretends monitoring elders in their day-to-day daily living activities in outdoor environments. In this context, this paper presents a specific proposal of application of the JDL Data Fusion Model to tracking old persons in outdoor environments. We additionally present the social interaction model in the context of the SafeRoute system, showing the interactions between caregivers and elders and including new contextual elements to make more efficient the tracking process.

Keywords

JDL Data Fusion Model Ambient Assisted Living Interaction model 

Notes

Acknowledgement

This work was partially funded by CNPq BJT Project 407851/2012-7, FAPERJ APQ1 Project 211.500/2015, FAPERJ APQ1 Project 211.451/2015, CNPq PVE Project 314017/2013-5, CNPq PEC-PG 190428/2013-9 and by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02.

References

  1. 1.
    Active Assisted Living Programme (2015). http://www.aal-europe.eu
  2. 2.
    Blázquez, G., Berlanga, A., Molina, J.: InContexto: multisensor architecture to obtain people context from smartphones. Int. J. Distrib. Sens. Netw. 2012, 1 (2012)CrossRefGoogle Scholar
  3. 3.
    Jiménez, J., Sánchez-Pi, N., Garcia, A.C.B.: Opportunistic sensoring using mobiles for tracking users in ambient intelligence. In: Mohamed, A., Novais, P., Pereira, A., González, G.V., Fernández-Caballero, A. (eds.) Ambient Intelligence-Software and Applications. AISC, vol. 376, pp. 111–123. Springer, Switzerland (2015)Google Scholar
  4. 4.
    Alemán, J.J., Sanchez-Pi, N., Garcia, A.C.B.: SafeRoute: an example of multi-sensoring tracking for the elderly using mobiles on ambient intelligence. In: Bajo, J., Hallenborg, K., Pawlewski, P., Botti, V., Sánchez-Pi, N., Duque Méndez, N.D., Lopes, F., Vicente, J. (eds.) PAAMS 2015 Workshops. CCIS, vol. 524, pp. 201–212. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  5. 5.
    Jiménez, J., Sánchez-Pi, N., Garcia, A.C.B.: Modeling social interactions for multi-sensory tracking of elders in outdoor environments on ambient assisted living. In: Simpsio Brasileiro sobre Fatores Humanos em Sistemas Computacionais (IHC 2015) (2015)Google Scholar
  6. 6.
    Fudickar, S., Schnor, B.: KopALa mobile orientation system for dementia patients. In: Tavangarian, D., Kirste, T., Timmermann, D., Lucke, U., Versick, D. (eds.) Intelligent Interactive Assistance and Mobile Multimedia Computing. CCIS, vol. 53, pp. 109–118. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Wan, J., et al.: Orange alerts: lessons from an outdoor case study. In: 5th International Conference on Pervasive Computing Technologies for Healthcare, pp. 446–451 (2011)Google Scholar
  8. 8.
    Roussaki, I., et al.: Hybrid context modeling: a location-based scheme using ontologies. In: 4th Annual IEEE International Conference on Pervasive Computing and Communications Workshop (2006)Google Scholar
  9. 9.
    Wang, X., et al.: Ontology based context modeling and reasoning using OWL. In: Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 18–22 (2004)Google Scholar
  10. 10.
    Akcay, O., Altan, O.: Ontology for context-aware visualization for spatial data in mobile devices. In: Joint Workshop Visualization and Exploration of Geospatial Data, vol. 36 (2007)Google Scholar
  11. 11.
    Hage, V., et al.: Design and use of the Simple Event Model (SEM). Web Semant. Sci. Serv. Agents World Wide Web 9, 128–136 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Javier Jiménez Alemán
    • 2
  • Nayat Sánchez-Pi
    • 1
  • Luis Marti
    • 2
  • José Manuel Molina
    • 3
  • Ana Cristina Bicharra Garcia
    • 2
  1. 1.Computer Science Department, Mathematics and Statistics InstituteRio de Janeiro State University (UERJ)Rio de Janeiro (RJ)Brazil
  2. 2.Institute of ComputingFluminense Federal University (UFF)Niteroi (RJ)Brazil
  3. 3.Computer Science DepartmentCarlos III University of Madrid (UC3M)MadridSpain

Personalised recommendations