Advertisement

Quantum Inspired High Dimensional Conceptual Space as KID Model for Elderly Assistance

  • M. S. Ishwarya
  • Ch. Aswani KumarEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

In this paper, we propose a cognitive system that acquires knowledge on elderly daily activities to ensure their wellness in a smart home using a Knowledge-Information-Data (KID) model. The novel cognitive framework called high dimensional conceptual space is proposed and used as KID model. This KID model is built using geometrical framework of conceptual spaces and formal concept analysis (FCA) to overcome imprecise concept notation of conceptual space with the help of topology based FCA. By doing so, conceptual space can be represented using Hilbert space. This high dimensional conceptual space is quantum inspired in terms of its concept representation. The knowledge learnt by the KID model recognizes the daily activities of the elderly. Consequently, the model identifies the scenario on which the wellness of the elderly has to be ensured.

Keywords

Cognition Concepts Conceptual spaces Formal concept analysis Quantum theory 

Notes

Acknowledgement

This research has received financial support from Department of Science and Technology, Government of India under the scheme Cognitive Science Research Initiative with grant number: SR/CSRI/118/2014.

References

  1. 1.
    Wang, Z., Yang, Z., Dong, T.: A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time. Sensors 17(2), 341 (2017)CrossRefGoogle Scholar
  2. 2.
    Wiederhold, M.D., Salva, A.M., Sotomayor, T., Coiro, C., Wiederhold, B.K.: Next Generation Stress Inoculation Training for Life Saving Skills Using Prosthetics, vol. 7, no. 1 (2009)Google Scholar
  3. 3.
    Huang, R., Mungai, P.K., Ma, J., Wang, K.I.-K.: Associative memory and recall model with KID model for human activity recognition. Futur. Gener. Comput. Syst. 92, 312–323 (2018)CrossRefGoogle Scholar
  4. 4.
    Ranasinghe, S., Al MacHot, F., Mayr, H.C.: A review on applications of activity recognition systems with regard to performance and evaluation. Int. J. Distrib. Sens. Netw. 12(8), 1550147716665520 (2016)Google Scholar
  5. 5.
    Sato, A., Huang, R.: A generic formulated KID model for pragmatic processing of data, information, and knowledge. In: Proceedings - 2015 IEEE 12th International Conference on Ubiquitous Intelligence and Computing. 2015 IEEE 12th International Conference on Autonomic and Trusted Computing. 2015 IEEE 15th International Conference on Scalable Computing and Communications, vol. 20, pp. 609–616 (2016)Google Scholar
  6. 6.
    Sato, A., Huang, R.: From data to knowledge: a cognitive approach to retail business intelligence. In: 2015 IEEE International Conference on Data Science and Data Intensive Systems, pp. 210–217 (2015)Google Scholar
  7. 7.
    Goguen, J.: What is a concept? In: Conceptual Structures: Common Semantics for Sharing Knowledge. Lecture Notes in Computer Science, vol. 3596, no. April, pp. 52–77 (2005)Google Scholar
  8. 8.
    Gärdenfors, P.: Conceptual Spaces: The Geometry of Thought, vol. 106, no. 3 (2000)Google Scholar
  9. 9.
    Belohlavek, R.: Introduction to formal concept analysis (2008)Google Scholar
  10. 10.
    Gärdenfors, P.: Representing actions and functional properties in conceptual spaces. In: Body, Language and Mind, no. Gärdenfors 2000, pp. 167–195 (2007)Google Scholar
  11. 11.
    Yearsley, J.M., Busemeyer, J.R.: Quantum cognition and decision theories: a tutorial. J. Math. Psychol. 74, 99–116 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Yearsley, J.M.: Advanced tools and concepts for quantum cognition: a tutorial. J. Math. Psychol. 78, 24–39 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Bruza, P.D., Busemeyer, J.R.: Quantum Models of Cognition and Decision (2012)Google Scholar
  14. 14.
    Lunardi, G.M., Al Machot, F., Shekhovtsov, V.A., Maran, V., Machado, G.M., Machado, A., Mayr, H.C., de Oliveira, J.P.M.: IoT-based human action prediction and support. Internet Things 3-4, 52–68 (2018)Google Scholar
  15. 15.
    Belohlavek, R.: Introduction to formal concept analysis. Palacky University, Department of Computer Science, Olomouc, p. 47 (2008)Google Scholar
  16. 16.
    Wang, Y.: On relation algebra: a denotational mathematical structure of relation theory for knowledge representation and cognitive computing. J. Adv. Math. Appl. 6(1), 43–66 (2017)CrossRefGoogle Scholar
  17. 17.
    Yang, K., Kim, E., Hwang, S., Choi, S.: Fuzzy concept mining based on formal concept analysis. Int. J. Comput. 2(3), 279–290 (2008)Google Scholar
  18. 18.
    Ravi, K., Ravi, V., Prasad, P.S.R.K.: Fuzzy formal concept analysis based opinion mining for CRM in financial services. Appl. Soft Comput. 60, 786–807 (2017)CrossRefGoogle Scholar
  19. 19.
    Aswani Kumar, C., Srinivas, S.: Concept lattice reduction using fuzzy K-Means clustering. Expert Syst. Appl. 37(3), 2696–2704 (2010)CrossRefGoogle Scholar
  20. 20.
    Kumar, C.A.: Fuzzy clustering-based formal concept analysis for association rules mining. Appl. Artif. Intell. 26(3), 274–301 (2012)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Belohlavek, R.: Representation of concept lattices by bidirectional associative memories, pp. 1–10 (1999)Google Scholar
  22. 22.
    Morales, F., de Toledo, P., Sanchis, A.: Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13(5), 5460–5477 (2013)CrossRefGoogle Scholar
  23. 23.
    de Souza Alves, T., de Oliveira, C.S., Sanin, C., Szczerbicki, E.: From knowledge based vision systems to cognitive vision systems: a review. Procedia Comput. Sci. 126, 1855–1864 (2018)Google Scholar
  24. 24.
    Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R.: Artificial cognition for social human–robot interaction: an implementation. Artif. Intell. 247, 45–69 (2017)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Meng, L., Miao, C., Leung, C.: Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing. Multimed. Tools Appl. 76(8), 10779–10799 (2017)CrossRefGoogle Scholar
  26. 26.
    Aswani Kumar, C., Ishwarya, M.S., Loo, C.K.: Formal concept analysis approach to cognitive functionalities of bidirectional associative memory. Biol. Inspired Cogn. Archit. 12, 20–33 (2015)Google Scholar
  27. 27.
    Shivhare, R., Cherukuri, A.K.: Establishment of cognitive relations based on cognitive informatics. Cogn. Comput. 9(5), 721–729 (2017)CrossRefGoogle Scholar
  28. 28.
    Arecchi, F.T.: A quantum uncertainty entails entangled linguistic sequences, arXiv preprint arXiv:1807.03174, pp. 1–14 (2018)
  29. 29.
    Muthukrishnan, A.K.: Information Retrieval using Concept Lattices, Dissertation University of Cincinnati (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVellore Institute of TechnologyVelloreIndia

Personalised recommendations