Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Research on Human Behavior Recognition based on Deep Neural Network

Authors
Shanshan Guan, Yinong Zhang, Zhuojing Tian
Corresponding Author
Yinong Zhang
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.124How to use a DOI?
Keywords
Behavior recognition; Deep learning; Filter; Behavior segmentation; SoftMax classifier.
Abstract
In order to improve the recognition rate of human behavior by intelligent terminals, a network model for deep learning of human behavior recognition is proposed. Time series data is transformed into a deep network model by performing motion segmentation using a sliding window algorithm. Feature vectors are imported into the SoftMax classifier through end-to-end research, which identifies six daily behaviors such as walking, sitting, going upstairs, going downstairs, standing and lying down. By comparing the recognition effects of different models, it was found that the convolutional neural network introduced into Dropout achieved better recognition results in UCI HAR dataset.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.124How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Shanshan Guan
AU  - Yinong Zhang
AU  - Zhuojing Tian
PY  - 2019/04
DA  - 2019/04
TI  - Research on Human Behavior Recognition based on Deep Neural Network
PB  - Atlantis Press
SP  - 777
EP  - 781
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.124
DO  - https://doi.org/10.2991/icmeit-19.2019.124
ID  - Guan2019/04
ER  -