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      MULTI-LABEL CLASSIFICATION FOR PHYSICAL ACTIVITY RECOGNITION FROM VARIOUS ACCELEROMETER SENSOR POSITIONS

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          Abstract

          In recent years, the use of accelerometers embedded in smartphones for Human Activity Recognition (HAR) has been well considered. Nevertheless, the role of the sensor placement is yet to  be  explored  and  needs  to  be  further  investigated. In this study, we investigated the role of sensor placements for recognizing various types of physical activities using the accelerometer sensor embedded in the smartphone. In fact, most of the reported work in HAR utilized traditional multi-class classification approaches to determine the types of activities. Hence, this study was to recognize the activity based on the best sensor placements that are appropriate to the activity performed. The traditional multi-class classification approach required more manual work and was time consuming to run the experiment separately. Thus, this study proposed the multi- label classification technique with the Label Combination (LC) approach in order to tackle this issue. The result was compared with several state-of-the-art traditional multi-class classification approaches. The multi-label classification result significantly outperformed the traditional multi-class classification methods as well as minimized the model build time.

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          Author and article information

          Contributors
          Malaysia
          Malaysia
          Malaysia
          Malaysia
          Malaysia
          Journal
          Journal of Information and Communication Technology
          UUM Press
          March 28 2018
          : 17
          : 209-231
          Affiliations
          [1 ]aculty of Computer Science and Information Technology Universiti Putra Malaysia, Selangor, Malaysia
          Article
          8252
          10.32890/jict2018.17.2.8252

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