Activity Recognition Based on a Multi-sensor Meta-classifier

  • Oresti Baños
  • Miguel Damas
  • Héctor Pomares
  • Ignacio Rojas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7903)


Ensuring ubiquity, robustness and continuity of monitoring is of key importance in activity recognition. To that end, multiple sensor configurations and fusion techniques are ever more used. In this paper we present a multi-sensor meta-classifier that aggregates the knowledge of several sensor-based decision entities to provide a unique and reliable activity classification. This model introduces a new weighting scheme which improves the rating of the impact that each entity has on the decision fusion process. Sensitivity and specificity are particularly considered as insertion and rejection weighting metrics instead of the overall accuracy classification performance proposed in a previous work. For the sake of comparison, both new and previous weighting models together with feature fusion models are tested on an extensive activity recognition benchmark dataset. The results demonstrate that the new weighting scheme enhances the decision aggregation thus leading to an improved recognition system.


Meta-classifier Sensor network Decision fusion Weighted decision Aggregation Activity recognition Human Behavior 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Oresti Baños
    • 1
  • Miguel Damas
    • 1
  • Héctor Pomares
    • 1
  • Ignacio Rojas
    • 1
  1. 1.Department of Computer Architecture and Computer Technology, Research Center for Information and Communications TechnologiesUniversity of Granada (CITIC-UGR)GranadaSpain

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