Research on Human Behavior Recognition based on Deep Neural Network
Shanshan Guan, Yinong Zhang, Zhuojing Tian
Available Online April 2019.
- https://doi.org/10.2991/icmeit-19.2019.124How to use a DOI?
- Behavior recognition; Deep learning; Filter; Behavior segmentation; SoftMax classifier.
- 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.
- 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 -