SEAbIRD: Sensor Activity Identification from Streams of Data

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 735)

Abstract

Active Aging is a proposal that aims to improve life quality, as a person grows old. One of the main use cases of this concept is the application of products and services based on technology; this approach, known as Ambient Assisted Living (AAL). An important activity performed by AAL is the discovery of the user’s activities of daily life (ADL) employing data retrieved from sensors set on an active home. Still, there is no much research on implementing a system for ADL discovery which contemplates factors as personalized configuration, sensor failure and user privacy. We identify the main requirements that an ADL discovery system must have. Then, we propose an ADL discovery schema that supports these necessities. Finally, we explore the application of adaptable and sensor-failure tolerant ADL discovery models over recorded data from a real user. This exploration evidences that our proposed models can adapt to the above-mentioned scenarios and still have an outstanding performance on activity discovery process.

Keywords

Ambient Assisted Living (AAL) Sensor data analysis Data stream mining 

Notes

Acknowledgments

This research was sponsored and supported by Alianza Caoba (Centro de Excelencia en Big Data y Data Analytics, Colombia). We thank our colleagues Claudia Roncancio, and Cyril Labbé from Universite Grenoble Alpes, LIG; and Paula Lago from Universidad de los Andes, who collaborate in our research project, provided expertise on the subjects under discussion and provided the datasets for the corresponding analysis.

References

  1. 1.
    Palacios, R.: The future of global ageing. Int. J. Epidemiol. 31, 786–791 (2002)CrossRefGoogle Scholar
  2. 2.
    Bloom, D.E., Boersch-Supan, A., McGee, P., Seike, A.: Population aging: facts, challenges, and responses. PGDA Work. Pap., no. 71 (2011)Google Scholar
  3. 3.
    Org, S.G., et al.: Envejecimiento activo: un marco político*, vol. 37, pp. 74–105 (2002)Google Scholar
  4. 4.
    Powell, J.L., Chen, S.: Technologies for Active Aging. Springer, London (2013)Google Scholar
  5. 5.
    World Health Organization, Noncommunicable diseases (2017). http://www.who.int/mediacentre/factsheets/fs355/en/. Accessed 19 May 2017
  6. 6.
    Chalmers, D.: Sensing and Systems in Pervasive Computing, 1st edn. Springer, London (2011)CrossRefGoogle Scholar
  7. 7.
    Lago, P., Jiménez-Guarín, C., Roncancio, C.: A case study on the analysis of behavior patterns and pattern changes in smart environments. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 296–303. Springer, Cham (2014). doi: 10.1007/978-3-319-13105-4_43 Google Scholar
  8. 8.
    Lark, Lark - About Us. http://www.web.lark.com/about/. Accessed 19 May 2017
  9. 9.
    Imai, S., Galli, A., Varela, C.A.: Dynamic data-driven avionics systems: inferring failure modes from data streams. Procedia Comput. Sci. 51, 1665–1674 (2015)CrossRefGoogle Scholar
  10. 10.
    INRIA, About - Amiqual4home. https://amiqual4home.inria.fr/home/. Accessed: 19 May 2017
  11. 11.
    Lago, P., Lang, F., Roncancio, C., Jiménez-Guarín, C., Mateescu, R., Bonnefond, N.: The ContextAct@A4H real-life dataset of daily-living activities. In: Brézillon, P., Turner, R., Penco, C. (eds.) CONTEXT 2017. LNCS, vol. 10257, pp. 175–188. Springer, Cham (2017). doi: 10.1007/978-3-319-57837-8_14 CrossRefGoogle Scholar
  12. 12.
    Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference Knowledge Discovery and Data Mining, KDD 2001, vol. 18, pp. 97–106 (2001)Google Scholar
  13. 13.
    Urwyler, P., et al.: Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers. Biomed. Eng. Online, 1–15 (2015)Google Scholar
  14. 14.
    Duque, A., Ordóñez, F.J., Toledo, P., Sanchis, A.: Offline and online activity recognition on mobile devices using accelerometer data. In: Bravo, J., Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 208–215. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-35395-6_29 CrossRefGoogle Scholar
  15. 15.
    Chen, L., Nugent, C., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974 (2012)CrossRefGoogle Scholar
  16. 16.
    Dimitrievski, A., Zdravevski, E., Lameski, P., Trajkovik, V.: Towards application of non-invasive environmental sensors for risks and activity detection. In: Proceedings of the 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing, ICCP 2016, pp. 27–33 (2016)Google Scholar
  17. 17.
    Amirjavid, F., Spachos, P., Plataniotis, K.N.: 3-D object localization in smart homes: a distributed sensor and video mining approach, pp. 1–10 (2017)Google Scholar
  18. 18.
    Lun, R., Gordon, C., Zhao, W.: Tracking the activities of daily lives: an integrated approach. In: Proceedings of the Future Technologies Conference, FTC 2016, December 2017Google Scholar
  19. 19.
    Negin, F., Cosar, S., Koperski, M., Bremond, F.: Generating unsupervised models for online long-term activity recognition. In: 3rd IAPR Asian Conference on Pattern Recognition (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computing and Systems Engineering Department, School of EngineeringUniversidad de los Andes, Bogotá, ColombiaBogotáColombia

Personalised recommendations