Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 8
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.
Authors and Affiliations
Kaori Fujinami, Tsubasa Saeki, Yinghuan Li, Tsuyoshi Ishikawa, Takuya Jimbo, Daigo Nagase, Koji Sato
Keywords
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- EP ID EP260652
- DOI 10.14569/IJACSA.2017.080858
- Views 102
- Downloads 0
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