Open AccessLSTM-CNN network for human activity recognition using WiFi CSI data
Author(s) -
Shunuo Shang,
QingYao Luo,
Jinjin Zhao,
Ruyi Xue,
Weihao Sun,
Nan Bao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012139
Subject(s) - computer science , activity recognition , convolutional neural network , software deployment , long short term memory , deep learning , artificial intelligence , channel state information , state (computer science) , machine learning , recurrent neural network , artificial neural network , wireless , telecommunications , algorithm , operating system
Human Activity Recognition (HAR) has had a diverse range of applications in various fields such as health, security and smart homes. Among different approaches of HAR, WiFi-based solutions are getting popular since it solves the problem of deployment cost, privacy concerns and restriction of the applicable environment. In this paper, we propose a WiFi-based human activity recognition system that can identify different activities via the channel state information from WiFi devices. A special deep learning framework, Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), is designed for accurate recognition. LSTM-CNN is going to be compared with the LSTM network and the experimental results demonstrate that LSTM-CNN outperforms existing models and has an average accuracy of 94.14% in multi-activity classification.