Paper Info
Title
On-Body Smartphone Localization With An Accelerometer
Abstract
A user of a smartphone may feel convenient, happy, safe, etc., if his/her smartphone works smartly based on his/her context or the context of the device. In this article, we deal with the position of a smartphone on the body and carrying items like bags as the context of a device. The storing position of a smartphone impacts the performance of the notification to a user, as well as the measurement of embedded sensors, which plays an important role in a device's functionality control, accurate activity recognition and reliable environmental sensing. In this article, nine storing positions, including four types of bags, are subject to recognition using an accelerometer on a smartphone. In total, 63 features are selected as a set of features among 182 systematically-defined features, which can characterize and discriminate the motion of a smartphone terminal during walking. As a result of leave-one-subject-out cross-validation, an accuracy of 0.801 for the nine-class classification is shown, while an accuracy of 0.859 is obtained against five classes, which merges the subclasses of trouser pockets and bags. We also show the basic performance evaluation to select the proper window size and classifier. Furthermore, the analysis of the contributive features is presented.
Year
DOI
Venue
2016
10.3390/info7020021
INFORMATION
Keywords
Field
DocType
smartphone, on-body position, device localization, accelerometer, machine learning, feature selection, activity recognition, opportunistic sensing, intelligent systems, wearable computing
Activity recognition,Feature selection,Intelligent decision support system,Wearable computer,Accelerometer,Computer science,Human–computer interaction,Environmental sensing,Artificial intelligence,Classifier (linguistics),Machine learning,Embedded system
Journal
Volume
Issue
Citations 
7
2
4
PageRank 
References 
Authors
0.45
28
1
Name
Order
Citations
PageRank
Kaori Fujinami131641.25