Research on Human Behavior Recognition based on Deep Neural Network

DOI

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.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

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
BT  - Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)
PB  - Atlantis Press
SP  - 777
EP  - 781
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.124
DO  - 10.2991/icmeit-19.2019.124
ID  - Guan2019/04
ER  -