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Knowledge Acquisition System based on JSON Schema for Electrophysiological Actuation

  • Nuno M. C. da CostaEmail author
  • Tiago Araujo
  • Neuza Nunes
  • Hugo Gamboa
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 455)

Abstract

Data stored and transferred through the Internet increases every day. The problem with these data begins with the lack of structure, making information disperse, uncorrelated, non-transparent and difficult to access and share. The World Wide Web Consortium (W3C), proposed a solution for this problem, Semantic Web, promoting semantic structured data, like ontologies, enabling machines to perform more work involved in finding, combining, and acting upon information on the web. Using this to our advantage we created a Knowledge Acquisition System, written in JavaScript using JavaScript Object Notation (JSON) as the data structure and JSON Schema to define that structure, enabling new ways of acquiring and storing knowledge semantically structured. A novel Human Computer Interaction framework was developed based on this knowledge system, providing a Electrophysiological Actuation Mechanism. We tested this mechanism by controlling an electrostimulator.

Keywords

Knowledge Acquisition System Human computer interaction Ontology Schema Language Electrophysiological actuation mechanism Electrostimulation Multi-purpose software 

Notes

Acknowledgements

I thank the co-authors for helping when was needed.

References

  1. 1.
    Prabhu, P.: Handbook of Human-Computer Interaction. North Holland, Amsterdam (1997)Google Scholar
  2. 2.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kDD process for extracting useful knowledge from volumes of data. Commun. ACM 39, 27–34 (1996)CrossRefGoogle Scholar
  3. 3.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. Mag. 284(5), 34–43 (2001)CrossRefGoogle Scholar
  4. 4.
    Thompson, H., et al.: XML schema. W3C working draft, May 2001 (2000)Google Scholar
  5. 5.
    Klarlund, N., Møller, A., Schwartzbach, M.: DSD: a schema language for xml. In: Proceedings of the Third Workshop on Formal Methods in Software Practice, pp. 101–111. ACM (2000)Google Scholar
  6. 6.
    Malmivuo, J., Plonsey, R.: Bioelectromagnetism, vol. 34. Peter Peregrinus Ltd, London (1996)Google Scholar
  7. 7.
    von Lewinski, F., Hofer, S., Kaus, J., Merboldt, K., Rothkegel, H., Schweizer, R., Liebetanz, D., Frahm, J., Paulus, W.: Efficacy of EMG-triggered electrical arm stimulation in chronic hemiparetic stroke patients. Restor. Neurol. Neurosci. 27, 189–197 (2009)Google Scholar
  8. 8.
    Cogan, S.: Neural stimulation and recording electrodes. Ann. Rev. Biomed. Eng. 10, 275–309 (2008)CrossRefGoogle Scholar
  9. 9.
    Perrigot, M., Pichon, B., Peskine, A., Vassilev, K.: Électrostimulation et rééducation périnéale de lincontinence urinaire et des troubles mictionnels non neurologiques. Annales de réadaptation et de médecine physique 51, 479–490 (2008). ElsevierCrossRefGoogle Scholar
  10. 10.
    Lebedev, V., Malygin, A., Kovalevski, A., Rychkova, S., Sisoev, V., Kropotov, S., Krupitski, E., Gerasimova, L., Glukhov, D., Kozlowski, G.: Devices for noninvasive transcranial electrostimulation of the brain endorphinergic system: application for improvement of human psycho-physiological status. Artif. organs 26, 248–251 (2002)CrossRefGoogle Scholar
  11. 11.
    Mayor, D., Micozzi, M.: Energy medicine east and west: a natural history of qi (paperback). Recherche 67, 02 (2011)Google Scholar
  12. 12.
    Le Tohic, A., Bastian, H., Pujo, M., Beslot, P., Mollard, R., Madelenat, P.: Effets de l’électrostimulation par veinoplus\(\textregistered \) sur les troubles circulatoires des membres inférieurs chez la femme enceinte. étude préliminaire. Gynécologie Obstétrique & Fertilité 37, 18–24 (2009)CrossRefGoogle Scholar
  13. 13.
    Motz, H., Rattay, F.: A study of the application of the Hodgkin-Huxley and the Frankenhaeuser-Huxley model for electrostimulation of the acoustic nerve. Neuroscience 18, 699–712 (1986)CrossRefGoogle Scholar
  14. 14.
    Siff, M.: Applications of electrostimulation in physical conditioning: a review. J. Strength Cond. Res. 4, 20 (1990)Google Scholar
  15. 15.
    Marqueste, T., Messan, F., Hug, F., Laurin, J., Dousset, E., Grelot, L., Decherchi, P.: Effect of repetitive biphasic muscle electrostimulation training on vertical jump performances in female volleyball players. Int. J. Sport Health Sci. 8, 50–55 (2010)CrossRefGoogle Scholar
  16. 16.
    Brocherie, F., Babault, N., Cometti, G., Maffiuletti, N., Chatard, J.: Electrostimulation training effects on the physical performance of ice hockey players. Med. Sci. Sports Exerc. 37, 455 (2005)CrossRefGoogle Scholar
  17. 17.
    Breen, P., Corley, G., O’Keeffe, D., Conway, C., ÓLaighin, G.: A programmable and portable NMES device for drop foot correction and blood flow assist applications. Med. Eng. Phys. 31, 400–408 (2009)CrossRefGoogle Scholar
  18. 18.
    Keller, T., Popovic, M., Pappas, I., Müller, P.: Transcutaneous functional electrical stimulator compex motion. Artif. Organs 26, 219–223 (2002)CrossRefGoogle Scholar
  19. 19.
    Suter, B.A., O’Connor, T., Iyer, V., Petreanu, L.T., Hooks, B.M., Kiritani, T., Svoboda, K., Shepherd, G.M.G.: Ephus: multipurpose data acquisition software for neuroscience experiments. Front. Neural Circ. 4, 100 (2010)Google Scholar
  20. 20.
    Prochazka, A., Gauthier, M., Wieler, M., Kenwell, Z.: The bionic glove: an electrical stimulator garment that provides controlled grasp and hand opening in quadriplegia. Arch. Phys. Med. Rehabil. 78, 608–614 (1997)CrossRefGoogle Scholar
  21. 21.
    Holzinger, A., Leitner, H.: Lessons from real-life usability engineering in hospital: from software usability to total workplace usability. In: Holzinger, A., Weidmann, K.-H. (eds.) Empowering Software Quality: How Can Usability Engineering Reach these Goals, pp. 153–160. Austrian Computer Society, Vienna (2005)Google Scholar
  22. 22.
    Broderick, B., Breen, P., ÓLaighin, G.: Electronic stimulators for surface neural prosthesis. J. Autom. Control 18, 25–33 (2008)CrossRefGoogle Scholar
  23. 23.
    Crockford, D.: The application/json media type for javascript object notation (JSON) (2006)Google Scholar
  24. 24.
    Zyp, K.: A JSON media type for describing the structure and meaning of JSON documents (2011)Google Scholar
  25. 25.
    Stoutemyer, D.: Can the Eureqa symbolic regression program, computer algebra and numerical analysis help each other? Arxiv preprint arXiv:1203.1023 (2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Nuno M. C. da Costa
    • 1
    Email author
  • Tiago Araujo
    • 1
  • Neuza Nunes
    • 1
  • Hugo Gamboa
    • 1
  1. 1.CEFITEC, Departamento de Fsica, FCTUniversidade Nova de LisboaLisbonPortugal

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