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Programmed Physical Activity for the Elderly as a Motor of Active Ageing

  • Galo SánchezEmail author
  • Valeria Lobina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)

Abstract

Our population is ageing, and this makes it necessary to implement initiatives that encourage sports activities among people over the age of 65. According to the concept of active ageing, keeping this age group active will allow to improve its health. This research aims to create an ageing model. To this end, we developed a platform for the capture of data and used it to conduct our study. To validate the proposed hypotheses, we examined the correlation between the data captured by the platform with medical data.

Keywords

Active ageing e-health 

References

  1. 1.
    Barrio Aliste, J.M.: De los problemas a los retos de la población rural de Castilla y León. Encrucijadas, Revista crítica de Ciencias Sociales 6, 117–128 (2013)Google Scholar
  2. 2.
    Gómez-Limón Rodríguez, J.A., Atance Muñiz, I., Rico González, M.: Percepción pública del problema de la despoblación del medio rural en Castilla y León. Ager, Revista de estudios sobre despoblación y desarrollo rural 6, 9–60 (2007)Google Scholar
  3. 3.
    Guillén García, F., Castro Sánchez, J.J., Guillén García, M.A.: Calidad de vida, salud y ejercicio físico: una aproximación al tema desde una perspectiva psicosocial. Revista Psicología del deporte 12, 91–110 (1997)Google Scholar
  4. 4.
    López Fernández, F.J.: Acción social en España. Centros, servicios y establecimientos de servicios sociales. ACCI, Madrid (2014)Google Scholar
  5. 5.
    Martínez de Haro, V. (coord.): Actividad física, salud y calidad de vida. UAM, Fundación Estudiantes, Madrid (2010)Google Scholar
  6. 6.
    OMS (ed.): Active Ageing: A Policy Framework. Organización Mundial de la Salud (2002). http://goo.gl/oG5w8M. Recuperado 18 Feb 2018
  7. 7.
    VVAA: Plan Integral para la actividad física y el deporte en personas mayores. Ministerio de Cultura (2009)Google Scholar
  8. 8.
    VVAA: Libro blanco del envejecimiento activo. CSD, Ministerio de Educación y Cultura (2010)Google Scholar
  9. 9.
    Li, T., Sun, S., Bolić, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Sig. Process. 119, 115–127 (2016).  https://doi.org/10.1016/j.sigpro.2015.07.013CrossRefGoogle Scholar
  10. 10.
    Lima, A.C.E.S., De Castro, L.N., Corchado, J.M.: A polarity analysis framework for Twitter messages. Appl. Math. Comput. 270, 756–767 (2015).  https://doi.org/10.1016/j.amc.2015.08.059CrossRefGoogle Scholar
  11. 11.
    Redondo-Gonzalez, E., De Castro, L.N., Moreno-Sierra, J., Maestro De Las Casas, M.L., Vera-Gonzalez, V., Ferrari, D.G., Corchado, J.M.: A cluster analysis. BioMed Res. Int. (2015).  https://doi.org/10.1155/2015/168682CrossRefGoogle Scholar
  12. 12.
    Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: Random finite set-based Bayesian filters using magnitude-adaptive target birth intensity. In: FUSION 2014 - 17th International Conference on Information Fusion (2014). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910637788&partnerID=40&md5=bd8602d6146b014266cf07dc35a681e0
  13. 13.
    Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., Chong, C.K., Chai, L.E., Omatu, S., Corchado, J.M.: Differential bees flux balance analysis with OptKnock for in silico microbial strains optimization. PLoS ONE 9(7) (2014).  https://doi.org/10.1371/journal.pone.0102744CrossRefGoogle Scholar
  14. 14.
    Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: A particle dyeing approach for track continuity for the SMC-PHD filter. In: FUSION 2014 - 17th International Conference on Information Fusion (2014). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910637583&partnerID=40&md5=709eb4815eaf544ce01a2c21aa749d8f
  15. 15.
    García Coria, J.A., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Expert Syst. Appl. 41(4 PART 1), 1189–1205 (2014).  https://doi.org/10.1016/j.eswa.2013.08.003CrossRefGoogle Scholar
  16. 16.
    Tapia, D.I., Fraile, J.A., Rodríguez, S., Alonso, R.S., Corchado, J.M.: Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems. Inf. Sci. 222, 47–65 (2013).  https://doi.org/10.1016/j.ins.2011.05.002CrossRefGoogle Scholar
  17. 17.
    Costa, Â., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Log. J. IGPL 20(4), 689–698 (2012).  https://doi.org/10.1093/jigpal/jzr021MathSciNetCrossRefGoogle Scholar
  18. 18.
    García, E., Rodríguez, S., Martín, B., Zato, C., Pérez, B.: MISIA: middleware infrastructure to simulate intelligent agents. In: Advances in Intelligent and Soft Computing, vol. 91 (2011).  https://doi.org/10.1007/978-3-642-19934-9_14Google Scholar
  19. 19.
    Rodríguez, S., De La Prieta, F., Tapia, D.I., Corchado, J.M.: Agents and computer vision for processing stereoscopic images. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (LNAI), vol. 6077 (2010).  https://doi.org/10.1007/978-3-642-13803-4_12Google Scholar
  20. 20.
    Rodríguez, S., Gil, O., De La Prieta, F., Zato, C., Corchado, J.M., Vega, P., Francisco, M.: People detection and stereoscopic analysis using MAS. In: Proceedings of INES 2010 - 14th International Conference on Intelligent Engineering Systems (2010).  https://doi.org/10.1109/INES.2010.5483855
  21. 21.
    Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf. Sci. 180(10), 2029–2043 (2010).  https://doi.org/10.1016/j.ins.2009.12.032CrossRefGoogle Scholar
  22. 22.
    Tapia, D.I., Corchado, J.M.: An ambient intelligence based multi-agent system for alzheimer health care. Int. J. Ambient. Comput. Intell 1(1), 15–26 (2009).  https://doi.org/10.4018/jaci.2009010102CrossRefGoogle Scholar
  23. 23.
    Mata, A., Corchado, J.M.: Forecasting the probability of finding oil slicks using a CBR system. Expert Syst. Appl. 36(4), 8239–8246 (2009).  https://doi.org/10.1016/j.eswa.2008.10.003CrossRefGoogle Scholar
  24. 24.
    Glez-Peña, D., Díaz, F., Hernández, J.M., Corchado, J.M., Fdez-Riverola, F.: geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research. BMC Bioinform. 10 (2009).  https://doi.org/10.1186/1471-2105-10-187CrossRefGoogle Scholar
  25. 25.
    Fernández-Riverola, F., Díaz, F., Corchado, J.M.: Reducing the memory size of a Fuzzy case-based reasoning system applying rough set techniques. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(1), 138–146 (2007).  https://doi.org/10.1109/TSMCC.2006.876058CrossRefGoogle Scholar
  26. 26.
    Méndez, J.R., Fdez-Riverola, F., Díaz, F., Iglesias, E.L., Corchado, J.M.: A comparative performance study of feature selection methods for the anti-spam filtering domain. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (LNAI), vol. 4065, pp. 106–120 (2006). https://www.scopus.com/inward/record.uri?eid=2-s2.0-33746435792&partnerID=40&md5=25345ac884f61c182680241828d448c5CrossRefGoogle Scholar
  27. 27.
    Fdez-Rtverola, F., Corchado, J.M.: FSfRT: forecasting system for red tides. Appl. Intell. 21(3), 251–264 (2004).  https://doi.org/10.1023/B:APIN.0000043558.52701.b1CrossRefGoogle Scholar
  28. 28.
    Corchado, J.M., Pavón, J., Corchado, E.S., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3155, pp. 547–559 (2004).  https://doi.org/10.1007/978-3-540-28631-8
  29. 29.
    Corchado, J.A., Aiken, J., Corchado, E.S., Lefevre, N., Smyth, T.: Quantifying the Ocean’s CO2 budget with a CoHeL-IBR system. In: Proceedings of Advances in Case-Based Reasoning, vol. 3155, pp. 533–546 (2004)Google Scholar
  30. 30.
    Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic system for business internal control. In: Industrial Conference on Data Mining, pp. 1–10 (2004).  https://doi.org/10.1007/978-3-540-30185-1_1Google Scholar
  31. 31.
    Corchado, J.M., Corchado, E.S., Aiken, J., Fyfe, C., Fernandez, F., Gonzalez, M.: Maximum likelihood hebbian learning based retrieval method for CBR systems. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2689, pp. 107–121 (2003).  https://doi.org/10.1007/3-540-45006-8_11
  32. 32.
    Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowl.-Based Syst. 16(5–6 SPEC.), 321–328 (2003).  https://doi.org/10.1016/S0950-7051(03)00034-0CrossRefGoogle Scholar
  33. 33.
    Glez-Bedia, M., Corchado, J.M., Corchado, E.S., Fyfe, C.: Analytical model for constructing deliberative agents. Int. J. Eng. Intell. Syst. Electr. Eng. Commun. 10(3), 173–185 (2002)Google Scholar
  34. 34.
    Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 32(4), 307–313 (2002).  https://doi.org/10.1109/tsmcc.2002.806072CrossRefGoogle Scholar
  35. 35.
    Fyfe, C., Corchado, J.: A comparison of Kernel methods for instantiating case based reasoning systems. Adv. Eng. Inform. 16(3), 165–178 (2002).  https://doi.org/10.1016/S1474-0346(02)00008-3CrossRefGoogle Scholar
  36. 36.
    Fyfe, C., Corchado, J.M.: Automating the construction of CBR systems using kernel methods. Int. J. Intell. Syst. 16(4), 571–586 (2001).  https://doi.org/10.1002/int.1024CrossRefzbMATHGoogle Scholar
  37. 37.
    Corchado, J.M., Fyfe, C.: Unsupervised neural method for temperature forecasting. Artif. Intell. Eng. 13(4), 351–357 (1999).  https://doi.org/10.1016/S0954-1810(99)00007-2CrossRefGoogle Scholar
  38. 38.
    Corchado, J., Fyfe, C., Lees, B.: Unsupervised learning for financial forecasting. In: Proceedings of the IEEE/IAFE/INFORMS 1998 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No. 98TH8367), pp. 259–263 (1998).  https://doi.org/10.1109/CIFER.1998.690316
  39. 39.
    Li, T.-C., Su, J.-Y., Liu, W., Corchado, J.M.: Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front. Inf. Technol. Electron. Eng. 18(12), 1913–1939 (2017)CrossRefGoogle Scholar
  40. 40.
    Wang, X., Li, T., Sun, S., Corchado, J.M.: A survey of recent advances in particle filters and remaining challenges for multitarget tracking. Sens. (Switz.) 17(12) (2017). Article No. 2707CrossRefGoogle Scholar
  41. 41.
    Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl. Based Syst. 137, 54–64 (2017)CrossRefGoogle Scholar
  42. 42.
    Pinto, T., Gazafroudi, A.S., Prieto-Castrillo, F., Santos, G., Silva, F., Corchado, J.M., Vale, Z.: Reserve costs allocation model for energy and reserve market simulation. In: 2017 19th International Conference on Intelligent System Application to Power Systems, ISAP 2017 (2017). Article No. 8071410Google Scholar
  43. 43.
    Lim, S.Y., Mohamad, M.S., Chai, L.E., Deris, S., Chan, W.H., Omatu, S., Corchado, J.M., Sjaugi, M.F., Zainuddin, M.M., Rajamohan, G., Ibrahim, Z., Yusof, Z.M.: Investigation of the effects of imputation methods for gene regulatory networks modelling using dynamic bayesian networks. In: DCAI 2016, pp. 413–421 (2016)CrossRefGoogle Scholar
  44. 44.
    Fernandes, F., Gomes, L., Morais, H., Silva, M.R., Vale, Z.A., Corchado, J.M.: Dynamic energy management method with demand response interaction applied in an office building. In: PAAMS (Special Sessions), pp. 69–82 (2016)Google Scholar
  45. 45.
    Dang, N.C., de la Prieta, F., Corchado, J.M., Moreno, M.N.: Framework for retrieving relevant contents related to fashion from online social network data. In: PAAMS (Special Sessions), pp. 335–347 (2016)Google Scholar
  46. 46.
    Chamoso, P., de la Prieta, F., de Paz, J.F., Corchado, J.M.: Swarm agent-based architecture suitable for internet of things and smartcities. In: DCA 2015, pp. 21–29 (2015)CrossRefGoogle Scholar
  47. 47.
    Omatu, S., Wada, T., Rodríguez, S., Chamoso, P., Corchado, J.M.: Multi-agent technology to perform odor classification. In: ISAmI 2014, pp. 241–252 (2014)Google Scholar
  48. 48.
    Román, J.Á., Rodríguez, S., Corchado, J.M.: Improving intelligent systems: specialization. In: PAAMS (Workshops), pp. 378–385 (2014)Google Scholar
  49. 49.
    Tapia, D.I., García, Ó., Alonso, R.S., Guevara, F., Catalina, J., Bravo, R.A., Corchado, J.M.: Evaluating the n-core polaris real-time locating system in an indoor environment. In: PAAMS (Workshops), pp. 29–37 (2012)Google Scholar
  50. 50.
    Tapia, D.I., Alonso, R.S., García, Ó., Corchado, J.M.: HERA: hardware-embedded reactive agents platform. In: PAAMS (Special Sessions), pp. 249–256 (2011)Google Scholar
  51. 51.
    Batista, Vivian Fad López Aguilar, R., Alonso, L., García, María Nand Moreno Corchado, J.M.: Data mining for grammatical inference with bioinformatics criteria. In: HAIS (2), pp. 53–60 (2010)Google Scholar
  52. 52.
    Mata, A., Lancho, B.P., Corchado, J.M.: Forest fires prediction by an organization based system. In: PAAMS, pp. 135–144 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.E.U. Magisterio de Zamora, Universidad de SalamancaZamoraSpain

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