Human Action Recognition on Cellphone Using Compositional Bidir-LSTM-CNN Networks

DOI

10.2991/cnci-19.2019.95How to use a DOI?

Keywords

Machine learning ,Compositional Bidir-LSTM-CNN, Accelerometer Sensors,Human Activity recognition.

Abstract

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.

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/).

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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  -