Recognition of Human Activities by Smartphone Sensors Using LSTM Neural Network

DOI

10.2991/icaita-18.2018.10How to use a DOI?

Keywords

deep learning; LSTM neural network; activity recognition; sensors of smartphone; TensorFlow

Abstract

Human activities have been a hot research field. Many sensors are embedded in the smartphone, which makes mobile sensor become available. Sensors of smartphone can early get the human activities information, which can analyze the human behaviors and provide the useful information to the human. In this paper, we propose a new method to recognize the human activities, which is based on the LSTM neural network to extract features and classify using accelerometer sensor data and gyroscope sensor data. Experimental results show that using LSTM neural network and TensorFlow deep learning open source architecture to extract motion state characteristics, this method achieves human activities classification with an accuracy of up to 90.4%.

Copyright

© 2018, 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/).

Download article (PDF)

TY  - CONF
AU  - Hong Zhao
AU  - Chunning Hou
AU  - Donglin Ma
PY  - 2018/03
DA  - 2018/03
TI  - Recognition of Human Activities by Smartphone Sensors Using LSTM Neural Network
BT  - Proceedings of the 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018)
PB  - Atlantis Press
SP  - 37
EP  - 40
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-18.2018.10
DO  - 10.2991/icaita-18.2018.10
ID  - Zhao2018/03
ER  -