Next Article in Journal
An Optical Fiber Lateral Displacement Measurement Method and Experiments Based on Reflective Grating Panel
Next Article in Special Issue
Robust Control for the Segway with Unknown Control Coefficient and Model Uncertainties
Previous Article in Journal
Integrated Toolset for WSN Application Planning, Development, Commissioning and Maintenance: The WSN-DPCM ARTEMIS-JU Project
Previous Article in Special Issue
An Improved Measurement Method for the Strength of Radiation of Reflective Beam in an Industrial Optical Sensor Based on Laser Displacement Meter
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(6), 812; doi:10.3390/s16060812

Investigating the Impact of Possession-Way of a Smartphone on Action Recognition

1
School of Computing, KAIST, Daejeon 34141, Korea
2
Naver Labs, Seongnam 13561, Korea
3
Samsung Electronics, Seoul 06765, Korea
4
Electrical and Computer Engineering Department, Khalifa University, Abu Dhabi 127788, UAE
This paper is an extended version of our paper published in Jeong, Y.-S.; Oh, K.-J.; Kim, Z.M.; Iraqi, Y.; Choi, H.-J. Does Smartphone Possession-Way Prediction Help Action Recognition? In Proceedings of the 16th International Symposium on Advanced Intelligent Systems, Mokpo, Korea, 4–7 November 2015; pp. 1228–1235.
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Suk-Seung Hwang, Euntai Kim, Sungshin Kim and Keon Myung Lee
Received: 20 April 2016 / Revised: 24 May 2016 / Accepted: 31 May 2016 / Published: 2 June 2016
View Full-Text   |   Download PDF [619 KB, uploaded 2 June 2016]   |  

Abstract

For the past few decades, action recognition has been attracting many researchers due to its wide use in a variety of applications. Especially with the increasing number of smartphone users, many studies have been conducted using sensors within a smartphone. However, a lot of these studies assume that the users carry the device in specific ways such as by hand, in a pocket, in a bag, etc. This paper investigates the impact of providing an action recognition system with the information of the possession-way of a smartphone, and vice versa. The experimental dataset consists of five possession-ways (hand, backpack, upper-pocket, lower-pocket, and shoulder-bag) and two actions (walking and running) gathered by seven users separately. Various machine learning models including recurrent neural network architectures are employed to explore the relationship between the action recognition and the possession-way recognition. The experimental results show that the assumption of possession-ways of smartphones do affect the performance of action recognition, and vice versa. The results also reveal that a good performance is achieved when both actions and possession-ways are recognized simultaneously. View Full-Text
Keywords: action recognition; possession-way recognition; artificial neural networks action recognition; possession-way recognition; artificial neural networks
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kim, Z.M.; Jeong, Y.-S.; Oh, H.R.; Oh, K.-J.; Lim, C.-G.; Iraqi, Y.; Choi, H.-J. Investigating the Impact of Possession-Way of a Smartphone on Action Recognition. Sensors 2016, 16, 812.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top