文章基本信息

标题：Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking
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作者：Kaori Fujinami ; Tsubasa Saeki ; Yinghuan Li 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2017
卷号：8
期号：8
DOI：10.14569/IJACSA.2017.080858
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Smartphone; on-body localization; accelerometer; machine learning; feature selection; wearable computing