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Knowledge Inference Through Analysis of Human Activities

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Intelligent Data Engineering and Automated Learning – IDEAL 2019 (IDEAL 2019)

Abstract

Monitoring human activities provides context data to be used by computational systems, aiming a better understanding of users and their surroundings. Uncertainty still is an obstacle to overcome when dealing with context-aware systems. The origin of it may be related to incomplete or outdated data. Attribute Grammars emerge as a consistent approach to deal with this problem due to their formal nature, allowing the definition of rules to validate context. In this paper, a model to validate human daily activities based on an Attribute Grammar is presented. Context data is analysed through the execution of rules that implement semantic statements. This processing, called semantic analysis, will highlight problems that can be raised up by uncertain situations. The main contribution of this paper is the proposal of a rigorous approach to deal with context-aware decisions (decisions that depend on the data collected from the sensors in the environment) in such a way that uncertainty can be detected and its harmful effects can be minimized.

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References

  1. Bobek, S., Nalepa, G.J.: Uncertainty handling in rule-based mobile context-aware systems. Pervasive Mob. Comput. 39, 159–179 (2017). https://doi.org/10.1016/j.pmcj.2016.09.004

    Article  Google Scholar 

  2. Burger, C., Karol, S., Wende, C.: Applying attribute grammars for metamodel semantics. In: ECOOP 2010 Workshop Proceedings - International Workshop on Formalization of Modeling Languages, FML 2010, January 2010. https://doi.org/10.1145/1943397.1943398

  3. Camara, J., Peng, W., Garlan, D., Schmerl, B.: Reasoning about sensing uncertainty and its reduction in decision-making for self-adaptation. Sci. Comput. Program. 167, 51–69 (2018). https://doi.org/10.1016/j.scico.2018.07.002

    Article  Google Scholar 

  4. Chahuara, P., Portet, F., Vacher, M.: Context-aware decision making under uncertainty for voice-based control of smart home. Expert Syst. Appl. 75, 63–79 (2017). https://doi.org/10.1016/j.eswa.2017.01.014

    Article  Google Scholar 

  5. Chen, S., Wang, Z., Liang, J., Yuan, X.: Uncertainty-aware visual analytics for exploring human behaviors from heterogeneous spatial temporal data. J. Vis. Lang. Comput. 48, 187–198 (2018). https://doi.org/10.1016/j.jvlc.2018.06.007

    Article  Google Scholar 

  6. Cook, D.J., Schmitter-Edgecombe, M.: Assessing the quality of activities in a smart environment. Methods Inf. Med. 48(5), 480–5 (2009)

    Article  Google Scholar 

  7. Freitas, L.O., Henriques, P.R., Novais, P.: Attribute grammar applied to human activities recognition in intelligent environments. In: Novais, P., Lloret, J., Chamoso, P., Carneiro, D., Navarro, E., Omatu, S. (eds.) ISAmI 2019. AISC, vol. 1006, pp. 62–70. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-24097-4_8

    Chapter  Google Scholar 

  8. Knuth, D.E.: Semantics of context-free languages. Math. Syst. Theory 2, 127–145 (1968)

    Article  MathSciNet  Google Scholar 

  9. Lim, B.Y., Dey, A.K.: Investigating intelligibility for uncertain context-aware applications. In: Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp 2011, pp. 415–424. ACM, New York (2011). https://doi.org/10.1145/2030112.2030168

  10. Noor, M.H.M., Salcic, Z., Wang, K.I.K.: Enhancing ontological reasoning with uncertainty handling for activity recognition. Know.-Based Syst. 114(C), 47–60 (2016). https://doi.org/10.1016/j.knosys.2016.09.028

    Article  Google Scholar 

  11. Ordónez, F.J., De Toledo, P., Sanchis, A.: Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sensors 13(5), 5460–5477 (2013). https://doi.org/10.3390/s130505460

    Article  Google Scholar 

  12. Tian, W., et al.: A review of uncertainty analysis in building energy assessment. Renew. Sustain. Energy Rev. 93, 285–301 (2018). https://doi.org/10.1016/j.rser.2018.05.029

    Article  Google Scholar 

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Acknowledgements

“This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.”

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Correspondence to Leandro O. Freitas .

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Freitas, L.O., Henriques, P.R., Novais, P. (2019). Knowledge Inference Through Analysis of Human Activities. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-33607-3_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33606-6

  • Online ISBN: 978-3-030-33607-3

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