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Sensor‐based activity recognition independent of device placement and orientation
Author(s) -
Shi Junhao,
Zuo Decheng,
Zhang Zhan,
Luo Danyan
Publication year - 2020
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3823
Subject(s) - computer science , convolutional neural network , activity recognition , orientation (vector space) , wearable computer , artificial intelligence , context (archaeology) , set (abstract data type) , distortion (music) , invariant (physics) , machine learning , pattern recognition (psychology) , computer vision , human–computer interaction , embedded system , computer network , mathematics , geometry , paleontology , amplifier , bandwidth (computing) , programming language , mathematical physics , biology
Human activity recognition (HAR) is a prominent subfield of pervasive computing and also provides context of many applications such as healthcare, education, and entertainment. Most wearable HAR studies assume that sensing device placement and orientation are fixed and never change. However, this condition is actually not always guaranteed in the real scenario and recognition result is influenced by the distortion as consequence. To handle this, our work proposes a new model based on convolutional neural network to extract robust features which are invariant of device placement and orientation, to train machine learning classifiers. We first carry out experiments to show negative effects of this problem. Then, we apply the convolutional neural network–based hybrid structure on the HAR. Results show that our method provides 15% to 40% accuracy promotion on public data set and 10% to 20% promotion on our own data set, both with distortion.