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Smartphone‐based activity recognition independent of device orientation and placement
Author(s) -
Guo Haodong,
Chen Ling,
Chen Gencai,
Lv Mingqi
Publication year - 2015
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3010
Subject(s) - computer science , orientation (vector space) , accelerometer , activity recognition , mobile device , precision and recall , artificial intelligence , computer vision , pattern recognition (psychology) , geometry , mathematics , operating system
Summary Traditional activity recognition methods based on accelerometers generally assume that the orientation and placement of sensing devices are fixed. However, the orientation and placement of smartphones, the most widely used sensing devices, usually cannot be controlled in daily lives. As a result, the activity recognition performance would be greatly affected. Aiming at this problem, this paper proposes a smartphone‐based activity recognition method, which is independent of device orientation and placement. First, the original 3D acceleration signals are mapped into a uniform reference coordinate system. Then, we apply multi‐dimensional motif discovery techniques for the smartphone‐based activity recognition problem. Experimental results show that our method can achieve better performance (with 85.2% precision and 82.9% recall) than other methods under the condition of unfixed orientation and placement. Copyright © 2015 John Wiley & Sons, Ltd.