Advertisement

Activity Recognition Enhancement Based on Ground-Truth: Introducing a New Method Including Accuracy and Granularity Metrics

  • Hamdi Aloulou
  • Romain Endelin
  • Mounir Mokhtari
  • Bessam Abdulrazak
  • Firas Kaddachi
  • Joaquim Bellmunt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10461)

Abstract

The uncertainty associated with existing sensing technologies and reasoning methods affects the outcome of the activity recognition process (e.g., accuracy, precision, granularity). The activity recognition process is even challenging when switching from laboratory towards real deployments, where scenarios are not predefined and more complex. Therefore we propose a novel method to improve the activity recognition outcome, by finding a proper balance between accuracy and granularity. The method has been validated through the deployment of UbiSMART (an AAL framework) in 45 scenarios of ageing in place. We discuss in this paper our method and the validation results.

Keywords

Ambient assisted living Activity recognition Semantic reasoning Quality insurance Ground-truth acquisition 

Notes

Acknowledgments

This research project has been supported by the Quality Of Life Chair supported by Foundation Telecom of the Institut Mines-Telecom in France, La Mutuelle Generale and REUNICA which figure among the major health-care insurance companies in France. The work is also supported by the grand emprunt VHP inter@ctive project. We also wish to acknowledge the support of the Saint-Vincent-de-Paul nursing home and its director Brigitte Choquet, who kindly let us deploy our system within their environment.

References

  1. 1.
    Abdulrazak, B., Malik, Y.: Review of challenges, requirements, and approaches of pervasive computing system evaluation. IETE Tech. Rev. 29(6), 506–522 (2012)CrossRefGoogle Scholar
  2. 2.
    Basili, V.R.: Software modeling and measurement: the goal/question/metric paradigm (1992)Google Scholar
  3. 3.
    Biswas, J., et al.: Activity recognition in assisted living facilities with incremental, approximate ground truth. In: Geissbühler, A., Demongeot, J., Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds.) ICOST 2015. LNCS, vol. 9102, pp. 103–115. Springer, Cham (2015). doi: 10.1007/978-3-319-19312-0_9 CrossRefGoogle Scholar
  4. 4.
    Chung, P.C., Liu, C.D.: A daily behavior enabled hidden markov model for human behavior understanding. Pattern Recogn. 41(5), 1572–1580 (2008)CrossRefzbMATHGoogle Scholar
  5. 5.
    Cook, D.J.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 2010(99), 1 (2010)Google Scholar
  6. 6.
    Fillenbaum, G.G.: Multidimensional Functional Assessment of Older Adults: The Duke Older Americans Resources and Services procedures. Psychology Press, London (2013)Google Scholar
  7. 7.
    Jahn, A., David, K.: Improved activity recognition by using grouped activities. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 1–5. IEEE (2016)Google Scholar
  8. 8.
    Jokela, T., Iivari, N., Matero, J., Karukka, M.: The standard of user-centered design and the standard definition of usability: analyzing ISO 13407 against ISO 9241–11. In: Proceedings of the Latin American Conference on Human-Computer Interaction, pp. 53–60. ACM (2003)Google Scholar
  9. 9.
    Kadouche, R., Abdulrazak, B., Mokhtari, M., Giroux, S., Pigot, H.: Semantic matching framework for handicap situation detection in smart environments. J. Ambient Intell. Smart Environ. 1(3), 223–234 (2009)Google Scholar
  10. 10.
    Katz, S., Ford, A.B., Moskowitz, R.W., Jackson, B.A., Jaffe, M.W.: Studies of illness in the aged: the index of adl: a standardized measure of biological and psychosocial function. JAMA 185(12), 914–919 (1963)CrossRefGoogle Scholar
  11. 11.
    Kleinberger, T., Jedlitschka, A., Storf, H., Steinbach-Nordmann, S., Prueckner, S.: An approach to and evaluations of assisted living systems using ambient intelligence for emergency monitoring and prevention. In: Stephanidis, C. (ed.) UAHCI 2009. LNCS, vol. 5615, pp. 199–208. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02710-9_23 CrossRefGoogle Scholar
  12. 12.
    Koch, K.-R.: Bayes’ Theorem. Bayesian Inference with Geodetic Applications. LNES, vol. 31, pp. 4–8. Springer, Heidelberg (1990). doi: 10.1007/BFb0048702 CrossRefGoogle Scholar
  13. 13.
    Lawton, M.P.: Scales to measure competence in everyday activities. Psychopharmacol. Bull. 24(4), 609–614 (1987)Google Scholar
  14. 14.
    Memon, M., Wagner, S.R., Pedersen, C.F., Beevi, F.H.A., Hansen, F.O.: Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes. Sensors 14(3), 4312–4341 (2014)CrossRefGoogle Scholar
  15. 15.
    Mokhtari, M., Aloulou, H., Tiberghien, T., Biswas, J., Racoceanu, D., Yap, P.: New trends to support independence in persons with mild dementia-a mini-review. Gerontology 58(6), 554–563 (2012)CrossRefGoogle Scholar
  16. 16.
    Slim, S.O., Atia, A., Mostafa, M.-S.M.: An experimental comparison between seven classification algorithms for activity recognition. In: Gaber, T., Hassanien, A.E., El-Bendary, N., Dey, N. (eds.) AISI 2015. AISC, vol. 407, pp. 37–46. Springer, Cham (2016). doi: 10.1007/978-3-319-26690-9_4 Google Scholar
  17. 17.
    Yared, R., Abdulrazak, B., Tessier, T., Mabilleau, P.: Cooking risk analysis to enhance safety of elderly people in smart kitchen. In: Proceedings of the 8th ACM International Conference on Pervasive Technologies Related to Assistive Environments, p. 12. ACM (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hamdi Aloulou
    • 1
    • 2
  • Romain Endelin
    • 1
    • 2
  • Mounir Mokhtari
    • 1
    • 2
    • 3
  • Bessam Abdulrazak
    • 4
  • Firas Kaddachi
    • 2
  • Joaquim Bellmunt
    • 1
    • 3
  1. 1.Institut Mines TelecomParisFrance
  2. 2.Laboratory of Informatics, Robotics and MicroelectronicsMontpellierFrance
  3. 3.Image and Pervasive Access LaboratorySingaporeSingapore
  4. 4.University of SherbrookeSherbrookeCanada

Personalised recommendations