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      • Department of Electrical and Electronics Engineering
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      Human activity classification with miniature inertial and magnetic sensor signals

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      Author
      Yüksek, Murat Cihan
      Barshan, Billur
      Date
      2011
      Source Title
      19th European Signal Processing Conference, 2011
      Print ISSN
      2219-5491
      Publisher
      IEEE
      Pages
      956 - 960
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      This study provides a comparative performance assessment of various pattern recognition techniques on classifying human activities that are performed while wearing miniature inertial and magnetic sensors. Activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial accelerometer, a tri-axial gyroscope, and a tri-axial magnetometer. The classification techniques compared in this study are: naïve Bayesian (NB), artificial neural networks (ANN), dissimilaritybased classifier (DBC), various decision-tree algorithms, Gaussian mixture model (GMM), and support vector machines (SVM). The methods that result in the highest correct differentiation rates are found to be GMM (99.1%), ANN (99.0%), and SVM (98.9%). © 2011 EURASIP.
      Keywords
      Classification technique
      Comparative performance assessment
      Decision-tree algorithm
      Differentiation rate
      Gaussian Mixture Model
      Human activities
      Pattern recognition techniques
      Sensor units
      Tri-axial magnetometer
      Triaxial accelerometer
      Intelligent agents
      Magnetic sensors
      Neural networks
      Pattern recognition
      Signal processing
      Support vector machines
      Permalink
      http://hdl.handle.net/11693/28269
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      • Department of Electrical and Electronics Engineering 3524
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