LSTM Networks for Mobile Human Activity Recognition
- 10.2991/icaita-16.2016.13How to use a DOI?
- Activity recognition, Deep learning, Long short memory network
A lot of real-life mobile sensing applications are becoming available. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. In this paper, we propose a LSTM-based feature extraction approach to recognize human activities using tri-axial accelerometers data. The experimental results on the (WISDM) Lab public datasets indicate that our LSTM-based approach is practical and achieves 92.1% accuracy.
- © 2016, 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 - Yuwen Chen AU - Kunhua Zhong AU - Ju Zhang AU - Qilong Sun AU - Xueliang Zhao PY - 2016/01 DA - 2016/01 TI - LSTM Networks for Mobile Human Activity Recognition BT - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications PB - Atlantis Press SP - 50 EP - 53 SN - 1951-6851 UR - https://doi.org/10.2991/icaita-16.2016.13 DO - 10.2991/icaita-16.2016.13 ID - Chen2016/01 ER -