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      Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units

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      Author
      Barshan, B.
      Yüksek, M. C.
      Date
      2014-11
      Source Title
      The Computer Journal
      Print ISSN
      0010-4620
      Publisher
      Oxford University Press
      Volume
      57
      Issue
      11
      Pages
      1649 - 1667
      Language
      English
      Type
      Article
      Item Usage Stats
      122
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      400
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      Abstract
      This study provides a comparative assessment on the different techniques of classifying human activities performed while wearing inertial and magnetic sensor units on the chest, arms and legs. The gyroscope, accelerometer and the magnetometer in each unit are tri-axial. Naive Bayesian classifier, artificial neural networks (ANNs), dissimilarity-based classifier, three types of decision trees, Gaussian mixture models (GMMs) and support vector machines (SVMs) are considered. A feature set extracted from the raw sensor data using principal component analysis is used for classification. Three different cross-validation techniques are employed to validate the classifiers. A performance comparison of the classifiers is provided in terms of their correct differentiation rates, confusion matrices and computational cost. The highest correct differentiation rates are achieved with ANNs (99.2%), SVMs (99.2%) and a GMM (99.1%). GMMs may be preferable because of their lower computational requirements. Regarding the position of sensor units on the body, those worn on the legs are the most informative. Comparing the different sensor modalities indicates that if only a single sensor type is used, the highest classification rates are achieved with magnetometers, followed by accelerometers and gyroscopes. The study also provides a comparison between two commonly used open source machine learning environments (WEKA and PRTools) in terms of their functionality, manageability, classifier performance and execution times. © 2013 © The British Computer Society 2013. All rights reserved.
      Keywords
      Accelerometer
      Body sensor networks
      Classifiers
      Cross validation
      Gyroscope
      Human activity classification
      Machine learning environments
      Magnetometer
      PRTools
      Wearable sensors
      WEKA
      Accelerometers
      Bayesian networks
      Decision trees
      Neural networks
      Principal component analysis
      Support vector machines
      Human activities
      Inertial sensor
      Learning environments
      Computer aided instruction
      Permalink
      http://hdl.handle.net/11693/21172
      Published Version (Please cite this version)
      http://dx.doi.org/10.1093/comjnl/bxt075
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      • Department of Electrical and Electronics Engineering 3524
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