Research of Comparison on Convolution Network and BP Network Based on Human Body Attitude Recognition
Authors
Yang Du, Jiaxin Tian, Binghong Zhan, Fei Guo
Corresponding Author
Yang Du
Available Online December 2018.
- DOI
- 10.2991/jimec-18.2018.36How to use a DOI?
- Keywords
- Acceleration Sensor; Human motion recognition; BP Network; Convolution Network; Python; TensorFlow; Deep Learning
- Abstract
In this thesis, after collecting data for human wearable acceleration sensor, human posture is recognized by using traditional BP network and convolution network based on Tensorflow in Python. Through respectively introducing and using BP network and convolution network, we then made a comparison experiment. It had become clear that the effect of using convolution network is much better than using BP network for the posture mentioned in the article. Its accuracy rate is 75%.
- 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/).
Cite this article
TY - CONF AU - Yang Du AU - Jiaxin Tian AU - Binghong Zhan AU - Fei Guo PY - 2018/12 DA - 2018/12 TI - Research of Comparison on Convolution Network and BP Network Based on Human Body Attitude Recognition BT - Proceedings of the 2018 3rd Joint International Information Technology，Mechanical and Electronic Engineering Conference (JIMEC 2018) PB - Atlantis Press SP - 169 EP - 172 SN - 2589-4943 UR - https://doi.org/10.2991/jimec-18.2018.36 DO - 10.2991/jimec-18.2018.36 ID - Du2018/12 ER -