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      Karşılıklı bilgi ölçütü kullanılarak giyilebilir hareket duyucu sinyallerinin aktivite tanıma amaçlı analizi

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      Author
      Dobrucalı, Oğuzcan
      Barshan, Billur
      Date
      2014-04
      Source Title
      22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings
      Publisher
      IEEE
      Pages
      1938 - 1941
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      Giyilebilir hareket duyucuları ile insan aktivitelerinin saptanmasında, uygun duyucu yapılanışının seçimi önem taşıyan bir konudur. Bu konu, kullanılacak duyucuların sayısının, türünün, sabitlenecekleri konum ve yönelimin belirlenmesi problemlerini içermektedir. Literatürde konuyla ilgili önceki çalışmalarda araştırmacılar, kendi seçtikleri duyucu yapılanışları ile diğer olası duyucu yapılanışlarını, söz konusu yapılanışlar ile insan aktivitelerini ayırt etme başarımlarına göre karşılaştırmışlardır. Ancak, söz konusu ayırt etme başarımlarının, kullanılan öznitelikler ve sınıflandırıcılara bağlı olduğu yadsınamaz. Bu çalışmada karşılıklı bilgi ölçütü kullanılarak duyucu yapılanışları, duyuculardan kaydedilen ham ölçümlerin zaman uzayındaki dağılımlarına göre belirlenmektedir. Bedenin farklı noktalarında bulunan ivmeölçer, dönüölçer ve manyetometrelerin ölçüm eksenleri arasından, gerçekleştirilen insan aktiviteleri hakkında en çok bilgi sağlayanları saptanmıştır.
       
      Selecting a suitable sensor configuration is an important aspect of recognizing human activities with wearable motion sensors. This problem encompasses selecting the number and type of the sensors, their position on the human body. In earlier works, researchers have used customized sensor configurations, and compared them with others in terms of the activity recognition rate. However, it is clear that these comparisons are dependent on the feature sets and classifiers employed. In this study, employing mutual information measure, sensor configurations are determined with respect to the time-domain distributions of the raw sensor measurements. The most informative axes of the accelerometers, gyroscopes, and magnetometers fixed at several locations on the human body are detected. © 2014 IEEE.
      Keywords
      Human activity recognition
      Mutual information
      Sensor configuration
      Wearable motion sensors
      Pattern recognition
      Signal processing
      Activity recognition
      Feature sets
      Human activities
      Human activity recognition
      Motion sensors
      Mutual information measures
      Mutual informations
      Sensor configurations
      Sensors
      Permalink
      http://hdl.handle.net/11693/26930
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2014.6830635
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      • Department of Electrical and Electronics Engineering 3524
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