Investigating the Impact of Possession-Way of a Smartphone on Action Recognition.
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.
School of Computing, KAIST, Daejeon 34141, Korea. zaemyung@kaist.ac.kr.
Zae Myung
ZM
Kim
Naver Labs, Seongnam 13561, Korea. pinode.waider@navercorp.com.
Young-Seob
YS
Jeong
Samsung Electronics, Seoul 06765, Korea. hyungrai.oh@samsung.com.
Hyung Rai
HR
Oh
School of Computing, KAIST, Daejeon 34141, Korea. aomaru@kaist.ac.kr.
Kyo-Joong
KJ
Oh
School of Computing, KAIST, Daejeon 34141, Korea. rayote@kaist.ac.kr.
Chae-Gyun
CG
Lim
Electrical and Computer Engineering Department, Khalifa University, Abu Dhabi 127788, UAE. Youssef.Iraqi@kustar.ac.ae.
Youssef
Y
Iraqi
School of Computing, KAIST, Daejeon 34141, Korea. hojinc@kaist.ac.kr.
Ho-Jin
HJ
Choi
action recognition; artificial neural networks; possession-way recognition