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Smartphone Data Analysis for Human Activity Recognition

  • Federico Concone
  • Salvatore Gaglio
  • Giuseppe Lo Re
  • Marco MoranaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10640)

Abstract

In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the user’s context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a participatory sensing paradigm where user’s feedbacks on recognised activities are exploited to update the inner models of the system. Experimental results show the effectiveness of our solution as compared to other state-of-the-art techniques.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Federico Concone
    • 1
  • Salvatore Gaglio
    • 1
  • Giuseppe Lo Re
    • 1
  • Marco Morana
    • 1
    Email author
  1. 1.DIID, University of PalermoPalermoItaly

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