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      • Department of Electrical and Electronics Engineering
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      Comparative study on classifying human activities with miniature inertial and magnetic sensors

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      Author(s)
      Altun, K.
      Barshan, B.
      Tunçel, O.
      Date
      2010
      Source Title
      Pattern Recognition
      Print ISSN
      0031-3203
      Publisher
      Elsevier
      Volume
      43
      Issue
      10
      Pages
      3605 - 3620
      Language
      English
      Type
      Article
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      Abstract
      This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). 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. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.
      Keywords
      Accelerometer
      Activity recognition and classification
      Artificial neural networks
      Bayesian decision making
      Decision tree
      Dynamic time warping
      Feature extraction
      Feature reduction
      Gyroscope
      Inertial sensors
      k-Nearest neighbor
      Least-squares method
      Magnetometer
      Rule-based algorithm
      Support vector machines
      Permalink
      http://hdl.handle.net/11693/22200
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.patcog.2010.04.019
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      • Department of Electrical and Electronics Engineering 3702
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