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DOI: 10.14569/IJACSA.2017.080858
PDF

Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

Author 1: Kaori Fujinami
Author 2: Tsubasa Saeki
Author 3: Yinghuan Li
Author 4: Tsuyoshi Ishikawa
Author 5: Takuya Jimbo
Author 6: Daigo Nagase
Author 7: Koji Sato

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 8, 2017.

  • Abstract and Keywords
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Abstract: Due to the dependency of our daily lives on smartphones, the states of the device have impact on the quality of services offered through a smartphone. In this article, we focus on the carrying states of the device while the user is walking, in which 17 states, e.g., in the front-left trouser pocket, calling phone in the right hand, in a backpack are subjects to recognition based on supervised learning with accelerometer-derived features. A large-scale data collection from 70 persons with three walking speeds allows reliable evaluation regarding suitable features and classifiers model, the feature selection method, robustness of localization against unknown person, and effect of walking speed in training a classifier. Person-independent evaluation shows that average F-measures of 17 class classification and merged 9 class classification were 0.823 and 0.913, respectively.

Keywords: Smartphone; on-body localization; accelerometer; machine learning; feature selection; wearable computing

Kaori Fujinami, Tsubasa Saeki, Yinghuan Li, Tsuyoshi Ishikawa, Takuya Jimbo, Daigo Nagase and Koji Sato, “Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking” International Journal of Advanced Computer Science and Applications(IJACSA), 8(8), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080858

@article{Fujinami2017,
title = {Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080858},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080858},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {8},
author = {Kaori Fujinami and Tsubasa Saeki and Yinghuan Li and Tsuyoshi Ishikawa and Takuya Jimbo and Daigo Nagase and Koji Sato}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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