Human Action Recognition on Cellphone Using Compositional Bidir-LSTM-CNN Networks
- 10.2991/cnci-19.2019.95How to use a DOI?
- Machine learning ,Compositional Bidir-LSTM-CNN, Accelerometer Sensors,Human Activity recognition.
Recently,the multimoal and high dimensional sensor data are prone to problems such as artificial error and time- consuming acquisition processes, especially in supervised human activity recognition. Therefore,this study proposes an activity recognition framework called compositional Bidir-LSTM-CNN Networks,which automatically extracts features from raw data using the optimized Convolutional Neural Network and further capture dynamic temporal features through the Bidirectional Lone Short Term Memory Network. Finally, this study paves the way for accurate recognition of human activities using the proposed framework with significantly improve 8% recognition accuracy along with additional features such as robustness and generalization.
- © 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 - Jiahao Wang AU - Qiuling Long AU - PiRahc AU - Kexuan Liu AU - Yingzi Xie PY - 2019/05 DA - 2019/05 TI - Human Action Recognition on Cellphone Using Compositional Bidir-LSTM-CNN Networks BT - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019) PB - Atlantis Press SP - 687 EP - 692 SN - 2352-538X UR - https://doi.org/10.2991/cnci-19.2019.95 DO - 10.2991/cnci-19.2019.95 ID - Wang2019/05 ER -