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      • Department of Electrical and Electronics Engineering
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      Minyatür eylemsizlik duyucuları ve manyetometre sinyallerinin işlenmesiyle insan aktivitelerinin sınıflandırılması

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      Author
      Yüksek, Murat Cihan
      Barshan, Billur
      Date
      2011-04
      Source Title
      IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011
      Publisher
      IEEE
      Pages
      1052 - 1055
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      Bu çalışmada insan vücuduna yerleştirilen minyatür eylemsizlik duyucuları ve manyetometreler kullanılarak çeşitli aktiviteler örüntü tanıma yöntemleriyle ayırdedilmiş ve karşılaştırmalı bir çalışmanın sonuçları sunulmuştur. Ayırdetme işlemi için basit Bayeşçi (BB) yöntem, yapay sinir ağları (YSA), benzeşmezlik tabanlı sınıflandırıcı (BTS), ceşitli karar ağacı (KA) yöntemleri, Gauss karışım modeli (GKM) ve destek vektör makinaları (DVM) kullanılmıştır. Aktiviteler gövdeye, kollara ve bacaklara takılan beş duyucu ünitesinden gelen verilerin işlenmesiyle ayırdedilmiştir. Her ünite, her biri üç-eksenli olmak üzere birer ivmeölçer, dönüölçer ve manyetometre içermektedir. Çalışmanın sonuçlarına göre, en iyi ilk üç başarı oranı sırasıyla GKM (%99.12), YSA (%99.09) ve DVM (%98.90) yöntemleri ile elde edilmiştir.
       
      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. Human 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: naive Bayesian (NB), artificial neural networks (ANN), dissimilarity-based classifier (DBC), various decision-tree methods, Gaussian mixture model (GMM), and support vector machines (SVM). According to the outcome of the study, the three methods that result in the highest correct differentiation rates are GMM (99.12%), ANN (99.09%), and SVM (99.80%).
      Keywords
      Artificial Neural Network
      Classification technique
      Comparative performance assessment
      Differentiation rate
      Gaussian Mixture Model
      Human activities
      Naive Bayesian
      Pattern recognition techniques
      Sensor units
      Tri-axial magnetometer
      Triaxial accelerometer
      Accelerometers
      Bayesian networks
      Intelligent agents
      Magnetic sensors
      Pattern recognition
      Signal processing
      Support vector machines
      Neural networks
      Permalink
      http://hdl.handle.net/11693/28389
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2011.5929835
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      • Department of Electrical and Electronics Engineering 3339

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