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HARF: A Hierarchical Activity Recognition Framework Using Smartphone Sensors

  • Manhyung Han
  • Jae Hun Bang
  • Chris Nugent
  • Sally McClean
  • Sungyoung Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8276)

Abstract

Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables utilizing different sources of sensor data. In this paper, we propose a smartphone based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user’s activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%.

Keywords

Activity Recognition Smartphone Multimodal Sensors Naïve Bayes Life-log 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Manhyung Han
    • 1
  • Jae Hun Bang
    • 1
  • Chris Nugent
    • 2
  • Sally McClean
    • 3
  • Sungyoung Lee
    • 1
  1. 1.Department of Computer EngineeringKyung Hee University (Global Campus)Korea
  2. 2.School of Computing and MathematicsUniversity of UlsterJordanstownU.K.
  3. 3.School of Computing and Information EngineeringUniversity of UlsterColeraineU.K.

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