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      •   BUIR Home
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      • Bilkent Theses
      • Theses - Department of Electrical and Electronics Engineering
      • Dept. of Electrical and Electronics Engineering - Master's degree
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      A comparative study on human activity classification with miniature inertial and magnetic sensors

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      Author(s)
      Yüksek, Murat Cihan
      Advisor
      Barshan, Billur
      Date
      2011
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
      Item Usage Stats
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      Abstract
      This study provides a comparative assessment on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques compared in this study are: naive Bayesian (NB) classifier, artificial neural networks (ANNs), dissimilarity-based classifier (DBC), various decision-tree methods, Gaussian mixture model (GMM), and support vector machines (SVM). The algorithms for these techniques are provided on two commonly used open source environments: Waikato environment for knowledge analysis (WEKA), a Java-based software; and pattern recognition toolbox (PRTools), a MATLAB toolbox. Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost. The methods that result in the highest correct differentiation rates are found to be ANN (99.2%), SVM (99.2%), and GMM (99.1%). The magnetometer is the best type of sensor to be used in classification whereas gyroscope is the least useful. Considering the locations of the sensor units on body, the sensors worn on the legs seem to provide the most valuable information.
      Keywords
      inertial sensors
      gyroscope
      accelerometer
      magnetometer
      activity recognition and classification
      feature extraction and reduction
      cross validation
      Bayesian decision making
      artificial neural networks
      support vector machines
      decision trees
      dissimilarity-based classifier
      Gaussian mixture model
      WEKA
      PRTools
      Permalink
      http://hdl.handle.net/11693/15616
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      • Dept. of Electrical and Electronics Engineering - Master's degree 655
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