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Knowledge Inference Through Analysis of Human Activities

  • Leandro O. FreitasEmail author
  • Pedro R. Henriques
  • Paulo Novais
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)

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 The origin of it may be related to incomplete or outdated data.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.

Keywords

Activity analysis Attribute grammar Uncertainty handling 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ALGORITMI CenterUniversity of MinhoBragaPortugal

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