Intelligent Control System for Back Pain Therapy

  • Juan A. Recio-GarciaEmail author
  • Belén Díaz-Agudo
  • Jose Luis Jorro-Aragoneses
  • Alireza Kazemi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10339)


Back pain is a pending subject in our society despite scientific advances. The Kazemi Back System (KBS) is a therapy machine that allows the patient to correctly perform manipulation exercises to heal or relieve pain. In this paper we describe and evaluate a CBR approach to suggest an stream of configuration values for the KBS machine based on previous sessions from the same patient or other similar patients. Its challenge is to capture the expertise knowledge of physiotherapists and reuse it for future therapies. The CBR system includes two complementary reuse processes and an explanation module. Within our experimental evaluation we discuss the problem of incompleteness and noise in the data and how to solve the cold start configuration for new patients.


  1. 1.
    Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
  2. 2.
    Ahmed, M.U., Begum, S., Funk, P., Xiong, N., von Schéele, B.: Case-based reasoning for diagnosis of stress using enhanced cosine and fuzzy similarity. In: Perner, P., Bichindaritz, L.S.I. (ed), 8th Industrial Conference, ICDM 2008, pp. 128–144. IBaI July 2008Google Scholar
  3. 3.
    Bach, K., Szczepanski, T., Aamodt, A., Gundersen, O.E., Mork, P.J.: Case representation and similarity assessment in the selfBACK decision support system. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS, vol. 9969, pp. 32–46. Springer, Cham (2016). doi: 10.1007/978-3-319-47096-2_3 CrossRefGoogle Scholar
  4. 4.
    Bichindaritz, I.: Case-based reasoning in the health sciences: why it matters for the health sciences and for CBR. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS, vol. 5239, pp. 1–17. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-85502-6_1 CrossRefGoogle Scholar
  5. 5.
    Díaz-Agudo, B., Recio-García, J.A., González-Calero, P.A.: Natural language queries in CBR systems. In: 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), vol. 2, pp. 468–472. IEEE Computer Society, Patras, Greece, 29–31 October 2007Google Scholar
  6. 6.
    Doyle, D., Cunningham, P., Walsh, P.: An evaluation of the usefulness of explanation in a CBR system for decision support in bronchiolitis treatment. In: Proceedings of the Workshop on Case-Based Reasoning in the Health Sciences, Workshop Programme at the Sixth International Conference on CaseBased Reasoning, pp. 32–41 (2005)Google Scholar
  7. 7.
    Horsburgh, B., Craw, S., Massie, S., Boswell, R.: Finding the hidden gems: recommending untagged music. In: Walsh, T. (ed) Proceedings of the 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011, pp. 2256–2261. IJCAI/AAAI, Barcelona, Catalonia, Spain, 16–22 July 2011Google Scholar
  8. 8.
    Montani, S., Portinale, L., Bellazzi, R., Leonardi, G.: RHENE: a case retrieval system for hemodialysis cases with dynamically monitored parameters. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS, vol. 3155, pp. 659–672. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-28631-8_48 CrossRefGoogle Scholar
  9. 9.
    Plaza, E., Arcos, J.-L.: Constructive adaptation. In: Craw, S., Preece, A. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 306–320. Springer, Heidelberg (2002). doi: 10.1007/3-540-46119-1_23 CrossRefGoogle Scholar
  10. 10.
    Recio, J.A., Díaz-Agudo, B., Gómez-Martín, M.A., Wiratunga, N.: Extending jCOLIBRI for textual CBR. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 421–435. Springer, Heidelberg (2005). doi: 10.1007/11536406_33 CrossRefGoogle Scholar
  11. 11.
    Recio-García, J.A., González-Calero, P.A., Díaz-Agudo, B.: jcolibri2: a framework for building case-based reasoning systems. Sci. Comput. Program. 79, 126–145 (2014)CrossRefGoogle Scholar
  12. 12.
    Quijano-Sánchez, L., Bridge, D., Díaz-Agudo, B., Recio-García, J.A.: A case-based solution to the cold-start problem in group recommenders. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS (LNAI), vol. 7466, pp. 342–356. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-32986-9_26 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Juan A. Recio-Garcia
    • 1
    Email author
  • Belén Díaz-Agudo
    • 1
  • Jose Luis Jorro-Aragoneses
    • 1
  • Alireza Kazemi
    • 2
  1. 1.Department of Software Engineering and Artificial Intelligence, Instituto de Tecnología del ConocimientoUniversidad Complutense de MadridMadridSpain
  2. 2.Institute of Physiotherapy and SportsGuadalajaraSpain

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