Abstract
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.
Supported by the UCM (Group 921330) and the Spanish Committee of Economy and Competitiveness (TIN2014-55006-R). The KBS machine is developed by Kazemi Back Health Inc. and funded by the Centre for the Development of Industrial Technology of the Spanish Committee of Economy and Competitiveness.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)
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 2008
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
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
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 2007
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)
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 2011
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
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
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
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)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Recio-Garcia, J.A., Díaz-Agudo, B., Jorro-Aragoneses, J.L., Kazemi, A. (2017). Intelligent Control System for Back Pain Therapy. In: Aha, D., Lieber, J. (eds) Case-Based Reasoning Research and Development. ICCBR 2017. Lecture Notes in Computer Science(), vol 10339. Springer, Cham. https://doi.org/10.1007/978-3-319-61030-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-61030-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61029-0
Online ISBN: 978-3-319-61030-6
eBook Packages: Computer ScienceComputer Science (R0)