Device-Free Activity Recognition for Underground Spaces Based on Convolutional Neural Network
Device-Free Activity Recognition for Underground Spaces Based on Convolutional Neural Network
- New search for: Zhou, Qizhen
- New search for: Xing, Jianchun
- New search for: Zhang, Xuewei
- New search for: Chen, Wei
- New search for: Zhou, Qizhen
- New search for: Xing, Jianchun
- New search for: Zhang, Xuewei
- New search for: Chen, Wei
Abstract Inspired by the excellent work of wireless sensing, we propose a non-invasive activity recognition system, Under-Sense, for underground space sensing with a pair of commodity Wi-Fi devices. Firstly, by extracting relative phase information from all 90 subcarriers, we construct fine-grained images and then compress the rectangle images into k-dimension by singular value decomposition (SVD). A nine-layer convolutional neural network (CNN) is designed to automatically extract important features from constructed images and classify five human activities. Our results show Under-Sense could achieve 99.5% average accuracy in the empty meeting room and 96.7% in complex student studio environment.
Document information
Device-Free Activity Recognition for Underground Spaces Based on Convolutional Neural Network
2019-01-01
9 pages
Article/Chapter (Book)
Electronic Resource
English
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