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Sensors 2016, 16(6), 812;

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

School of Computing, KAIST, Daejeon 34141, Korea
Naver Labs, Seongnam 13561, Korea
Samsung Electronics, Seoul 06765, Korea
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
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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

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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.

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