Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

LSTM Networks for Mobile Human Activity Recognition

Authors
Yuwen Chen, Kunhua Zhong, Ju Zhang, Qilong Sun, Xueliang Zhao
Corresponding Author
Yuwen Chen
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.13How to use a DOI?
Keywords
Activity recognition, Deep learning, Long short memory network
Abstract
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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.13How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

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  - https://doi.org/10.2991/icaita-16.2016.13
ID  - Chen2016/01
ER  -