Gamification and Information Fusion for Rehabilitation: An Ambient Assisted Living Case Study

  • Javier Jiménez Alemán
  • Nayat Sanchez-PiEmail author
  • Luis Martí
  • José Manuel Molina
  • Ana Cristina Bicharra García
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9755)


Nowadays elders, often find it difficult to keep track of their cognitive and functional abilities required for remaining independent in their homes. Ambient Assisted Living (AAL) are the Ambient Intelligence based technologies for the support of daily activities to elders. Traditional rehabilitation is an example of a common activity elders may require and that usually implies they move to the rehabilitation clinics, which is the main reason for treatment discontinuation. Tele-rehabilitation is a solution that not only may help elders but also their family members and health professionals to monitor elder’s treatment. The purpose of this paper is to present a tele-rehabilitation system that uses the motion-tracking sensor of the Kinect, to allow the elderly users natural interaction, combined with a set of external sensors as a form of input. Data fusion techniques are applied in order to integrate these data for detecting right movements and to monitor elder’s treatment in the rehabilitation process.


Gamification Data fusion Ambient assisted living Human-computer interaction 



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


  1. 1.
    Ahlberg, S., Hörling, P., Johansson, K., Jöred, K., Kjellström, H., Mårtenson, C., Neider, G., Schubert, J., Svenson, P., Svensson, P., et al.: An information fusion demonstrator for tactical intelligence processing in network-based defense. Inf. Fusion 8(1), 84–107 (2007)CrossRefGoogle Scholar
  2. 2.
    Aldinger, T., Kao, J.: Data fusion and theater undersea warfare-an oceanographer’s perspective. In: OCEANS 2004. MTTS/IEEE TECHNO-OCEAN 2004, vol. 4, pp. 2008–2012. IEEE (2004)Google Scholar
  3. 3.
    Bashi, A.: Fault Detection for Systems with Multiple Unknown Modes and Similar Units. Ph.D. thesis, University of New Orleans (2010)Google Scholar
  4. 4.
    Bashi, A., Jilkov, V.P., Li, X.R.: Fault detection for systems with multiple unknown modes and similar units - Part I. In: 12th International Conference on Information Fusion (FUSION 2009), pp. 732–739. IEEE (2009)Google Scholar
  5. 5.
    Basir, O., Yuan, X.: Engine fault diagnosis based on multi-sensor information fusion using dempster-shafer evidence theory. Inf. Fusion 8(4), 379–386 (2007)CrossRefGoogle Scholar
  6. 6.
    Blasch, E., Kadar, I., Salerno, J., Kokar, M.M., Das, S., Powell, G.M., Corkill, D.D., Ruspini, E.H.: Issues and challenges of knowledge representation and reasoning methods in situation assessment (level 2 fusion). In: Defense and Security Symposium, p. 623510. International Society for Optics and Photonics (2006)Google Scholar
  7. 7.
    Blasch, E., Llinas, J., Lambert, D., Valin, P., Das, S., Chong, C., Kokar, M., Shahbazian, E.: High level information fusion developments, issues, and grand challenges: fusion 2010 panel discussion. In: 2010 13th Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2010)Google Scholar
  8. 8.
    Blázquez Gil, G., Berlanga, A., Molina, J.M.: Incontexto: multisensor architecture to obtain people context from smartphones. Int. J. Distrib. Sens. Netw. 2012 (2012)Google Scholar
  9. 9.
    Chong, C.Y., Liggins, M., et al.: Fusion technologies for drug interdiction. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 1994), pp. 435–441. IEEE (1994)Google Scholar
  10. 10.
    Corona, I., Giacinto, G., Mazzariello, C., Roli, F., Sansone, C.: Information fusion for computer security: state of the art and open issues. Inf. Fusion 10(4), 274–284 (2009)CrossRefGoogle Scholar
  11. 11.
    Deterding, S., Sicart, M., Nacke, L., O’Hara, K., Dixon, D.: Gamification. using game-design elements in non-gaming contexts. In: CHI 2011 Extended Abstracts on Human Factors in Computing Systems, pp. 2425–2428. ACM (2011)Google Scholar
  12. 12.
    Gad, A., Farooq, M.: Data fusion architecture for maritime surveillance. In: Proceedings of the Fifth International Conference on Information Fusion (FUSION 2002), vol. 1, pp. 448–455. IEEE (2002)Google Scholar
  13. 13.
    Giacinto, G., Roli, F., Sansone, C.: Information fusion in computer security. Inf. Fusion 10(4), 272–273 (2009)CrossRefGoogle Scholar
  14. 14.
    Gómez-Romero, J., Patricio, M.A., García, J., Molina, J.M.: Ontological representation of context knowledge for visual data fusion. In: 12th International Conference on Information Fusion (FUSION 2009), pp. 2136–2143. IEEE (2009)Google Scholar
  15. 15.
    Heiden, U., Segl, K., Roessner, S., Kaufmann, H.: Ecological evaluation of urban biotope types using airborne hyperspectral hymap data. In: 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pp. 18–22. IEEE (2003)Google Scholar
  16. 16.
    Hubert-Moy, L., Corgne, S., Mercier, G., Solaiman, B.: Land use and land cover change prediction with the theory of evidence: a case study in an intensive agricultural region of france. In: Proceedings of the Fifth International Conference on Information Fusion (FUSION 2002), vol. 1, pp. 114–121. IEEE (2002)Google Scholar
  17. 17.
    Jiménez Alemán, J., Sanchez-Pi, N., Bicharra Garcia, A.C.: Opportunistic sensoring using mobiles for tracking users in ambient intelligence. In: Mohamed, A., Novais, P., Pereira, A., Villarrubia-González, G., Fernández-Caballero, A. (eds.) Ambient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence (ISAmI 2015). Advances in Intelligent Systems and Computing, vol. 376, pp. 115–123. Springer, Heidelberg (2015). Google Scholar
  18. 18.
    Khalil, A., Gill, M.K., McKee, M.: New applications for information fusion and soil moisture forecasting. In: 8th International Conference on Information Fusion (FUSION 2005), vol. 2, p. 7. IEEE (2005)Google Scholar
  19. 19.
    Koch, S., Hägglund, M.: Health informatics and the delivery of care to older people. Maturitas 63(3), 195–199 (2009)CrossRefGoogle Scholar
  20. 20.
    Korn, O., Brach, M., Schmidt, A., Hörz, T., Konrad, R.: Context-sensitive user-centered scalability: an introduction focusing on exergames and assistive systems in work contexts. In: Göbel, S., Müller, W., Urban, B., Wiemeyer, J. (eds.) GameDays 2012 and Edutainment 2012. LNCS, vol. 7516, pp. 164–176. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Liggins, M.E., Bramson, A., et al.: Off-board augmented fusion for improved target detection and track. In: 1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, pp. 295–299. IEEE (1993)Google Scholar
  22. 22.
    Little, E.G., Rogova, G.L.: Ontology meta-model for building a situational picture of catastrophic events. In: 8th International Conference on Information Fusion (FUSION 2005), vol. 1, pp. 1–8. IEEE (2005)Google Scholar
  23. 23.
    Llinas, J.: Information fusion for natural and man-made disasters. In: Proceedings of the Fifth International Conference on Information Fusion (FUSION 2002), vol. 1, pp. 570–576. IEEE (2002)Google Scholar
  24. 24.
    Llinas, J., Moskal, M., McMahon, T.: Information fusion for nuclear, chemical, biological & radiological (NCBR) battle management support/disaster response management support. Technical report, Center for MultiSource Information Fusion, School of Engineering and Applied Sciences, University of Buffalo, USA (2002)Google Scholar
  25. 25.
    Mattioli, J., Museux, N., Hemaissia, M., Laudy, C.: A crisis response situation model. In: 10th International Conference on Information Fusion (FUSION 2007), pp. 1–7. IEEE (2007)Google Scholar
  26. 26.
    Sanchez-Pi, N.: Intelligent techniques for context-aware systems. Ph.D. thesis, Departmento de Informática, Universidad Carlos III de Madrid, Colmenarejo, Spain (2011)Google Scholar
  27. 27.
    Sanchez-Pi, N., Martí, L., Molina, J.M., Garcia, A.C.B.: High-level information fusion for risk and accidents prevention in pervasive oil industry environments. In: Corchado, J.M., et al. (eds.) PAAMS 2014. CCIS, vol. 430, pp. 202–213. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  28. 28.
    Sanchez-Pi, N., Martí, L., Molina, J.M., Garcia, A.C.B.: An information fusion framework for context-based accidents prevention. In: 2014 Proceedings of the 17th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2014)Google Scholar
  29. 29.
    Sánchez-Pi, N., Molina, J.M.: A centralized approach to an ambient assisted living application: an intelligent home. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 706–709. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Javier Jiménez Alemán
    • 1
  • Nayat Sanchez-Pi
    • 2
    Email author
  • Luis Martí
    • 1
  • José Manuel Molina
    • 3
  • Ana Cristina Bicharra García
    • 1
  1. 1.Institute of ComputingFluminense Federal UniversityNiteróiBrazil
  2. 2.Institute of Mathematics and StatisticsRio de Janeiro State UniversityRio de JaneiroBrazil
  3. 3.Computer Science DepartmentCarlos III University of MadridMadridSpain

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