An Efficient Technique for Real-Time Human Activity Classification Using Accelerometer Data

  • Giorgio Biagetti
  • Paolo CrippaEmail author
  • Laura Falaschetti
  • Simone Orcioni
  • Claudio Turchetti
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)


Accurate estimation of biometric parameters recorded from subjects’ wrist or waist, when the subjects are performing various physical exercises, is often a challenging problem due to the presence of motion artifacts. In order to reduce the motion artifacts, data derived from a triaxial accelerometer have been proven to be very useful. Unfortunately, wearable devices such as smartphones and smartwatches are in general differently oriented during real life activities, so the data derived from the three axes are mixed up. This paper proposes an efficient technique for real-time recognition of human activities by using accelerometer data that is based on singular value decomposition (SVD) and truncated Karhunen-Loève transform (KLT) for feature extraction and reduction, and Bayesian classification for class recognition, that is independent of the orientation of the sensor. This is particularly suitable for implementation in wearable devices. In order to demonstrate the validity of this technique, it has been successfully applied to a database of accelerometer data derived from static postures, dynamic activities, and postural transitions occurring between the static postures.


Activity detection Accelerometer Real-time Smartphone Health Fitness Bayesian classification Singular value decomposition SVD Expectation maximization EM Feature extraction 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Giorgio Biagetti
    • 1
  • Paolo Crippa
    • 1
    Email author
  • Laura Falaschetti
    • 1
  • Simone Orcioni
    • 1
  • Claudio Turchetti
    • 1
  1. 1.DII – Dipartimento di Ingegneria dell’InformazioneUniversità Politecnica delle MarcheAnconaItaly

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