Advertisement

A Data Quality Model for AAL Systems

  • Lenin Erazo-Garzon
  • Jean Erraez
  • Lourdes Illescas-Peña
  • Priscila CedilloEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1099)

Abstract

Ambient Assisted Living (AAL) aims to improve the quality of life of people, supporting them in their daily activities by the use of information technologies. The AAL systems are preferably focused on vulnerable groups (e.g., elderly people, people with special needs, children, patients with chronic diseases) to increase their independence in their natural living environment. These systems work in real time and their correct operation depends on the inferred knowledge of the data collected from sensors or other sources, thus the assurance of the quality of data is a priority aspect, especially in those systems that can put in risk the life of people. Currently, the research in this line is limited, there are not data quality models with a complete set of attributes and metrics adjusted to the AAL domain. This work proposes a data quality model for AAL systems conformed by the following characteristics: accuracy, completeness, currentness, confidentiality, accessibility, understandability and compliance; and their corresponding metrics. Finally, the application of the model to an intelligent pillbox, showed that it is a complete and valuable tool to evaluate the degree to which the quality of data is reached and preserved by an AAL system.

Keywords

AAL Ambient Assisted Living Evaluation Data Measurement Metric Quality model 

Notes

Acknowledgements

This study is part of the research projects: (i) Software product, data and context quality model for AAL systems, LIDI - Universidad del Azuay; (ii) Design of architectures and interaction models for assisted living environments aimed at older adults. Case study: playful and social environments, XVIII DIUC Call for Research Projects; and, (iii) Fog Computing applied to monitoring devices used in assisted living environments; Case study: platform for the elderly, XVII DIUC Call for Research Projects. Therefore, we thank to Universidad del Azuay and DIUC of Universidad de Cuenca for their support.

References

  1. 1.
    McNaull, J., Augusto, J.C., Mulvenna, M., McCullagh, P.: Data and information quality issues in ambient assisted living systems. J. Data Inf. Qual. (JDIQ) 4(1), 1–15 (2012)CrossRefGoogle Scholar
  2. 2.
    AAL-Europe - Active and Assisted Living Programme. http://www.aal-europe.eu/about/. Accessed 20 May 2019
  3. 3.
    Department of Economic and Social Affairs Population Division: World population ageing 2015. United Nations, New York (2015)Google Scholar
  4. 4.
    Garcés, L., Ampatzoglou, A., Avgeriou, P., Nakagawa, E.Y.: Quality attributes and quality models for ambient assisted living software systems: a systematic mapping. Inf. Softw. Technol. 82, 121–138 (2017)CrossRefGoogle Scholar
  5. 5.
    European Commission - Ambient assisted living - Preparation of an art. 169-initiative. https://cordis.europa.eu/project/rcn/71922/factsheet/en. Accessed 20 May 2019
  6. 6.
    ISO/IEC 25012:2008 Data Quality Model. https://iso25000.com/index.php/en/iso-25000-standards/iso-25012. Accessed 20 May 2019
  7. 7.
    Kara, M., Lamouchi, O., Ramdane-Cherif, A.: A quality model for the evaluation AAL systems. Procedia Comput. Sci. 113, 392–399 (2017)CrossRefGoogle Scholar
  8. 8.
    Hallerstede, S., Aysha Beevi, F.H., Pedersen, C.F., Wagner, S.: Data quality oriented taxonomy of ambient assisted living systems. In: IET International Conference on Technologies for Active and Assisted Living (TechAAL), London, UK (2015)Google Scholar
  9. 9.
    Wagner, S., et al.: CareStore platform for seamless deployment of ambient assisted living applications and devices. In: 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, pp. 240–243. IEEE, Venice (2013)Google Scholar
  10. 10.
    World Health Organization: International classification of functioning, disability and health ICF. World Health Organization, Geneva (2001)Google Scholar
  11. 11.
    Beevi, F.H.A., Wagner, S., Pedersen, C., Hallerstede, S.: Data quality oriented efficacy evaluation method for ambient assisted living technologies. In: 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, pp. 235–240. ACM, Cancun (2016)Google Scholar
  12. 12.
    Huzooree, G., Khedo, K.K., Joonas, N.: Data reliability and quality in body area networks for diabetes monitoring. In: EAI/Springer Innovations in Communication and Computing, pp. 55–86. Springer, Cham (2019)Google Scholar
  13. 13.
    Olsina, L., Rossi, G.: Measuring web application quality with WebQEM. IEEE Multimedia 9(4), 20–29 (2002)CrossRefGoogle Scholar
  14. 14.
    Parra, J.M., Valdez, W.F., Guevara, A.P., Cedillo, I.P., Ortíz-Segarra, J.: Intelligent PillBox: automatic and programmable assistive technology device. In: 13th IASTED International Conference on Biomedical Engineering, pp. 74–81. IEEE, Innsbruck (2017)Google Scholar
  15. 15.
    Solís, W.V., Cedillo, P., Parra, J., Guevara, A., Ortiz, J.: Intelligent pillbox: evaluating the user perceptions of elderly people. In: 26th International Conference on Information Systems Development. University of Central Lancashire, Larnaca, Cyprus (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Lenin Erazo-Garzon
    • 1
    • 2
  • Jean Erraez
    • 1
  • Lourdes Illescas-Peña
    • 2
  • Priscila Cedillo
    • 1
    • 2
    Email author
  1. 1.Universidad del AzuayCuencaEcuador
  2. 2.Universidad de CuencaCuencaEcuador

Personalised recommendations