TY - GEN
T1 - Activity Recognition Using the Joint of Wi-Fi 2.4G and 5G Frequency Bands
AU - Yan, Beiming
AU - Cheng, Wei
AU - Huang, Getong
AU - Zhu, Zhong Shang
AU - Gao, Xiang
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Human activity recognition based on Wi-Fi Channel State Information (CSI) is playing an increasingly important role in various fields such as security, medical care, etc. Most existing CSI-based activity identification methods rely on a single frequency band of Wi-Fi signals. In addition, the use of deep learning methods for CSI-based activity recognition is still in its infancy. In this paper, we propose a scheme of activity recognition using the joint CSI of the 2.4G frequency band and the 5G frequency band (ARJF), which takes advantage of a novel convolutional neural network (CNN) to automatically extract deep features from the CSI data, to realize the detection and classification of 7 actions. Compared with the recognition result of a single frequency band, the proposed method has better recognition accuracy.
AB - Human activity recognition based on Wi-Fi Channel State Information (CSI) is playing an increasingly important role in various fields such as security, medical care, etc. Most existing CSI-based activity identification methods rely on a single frequency band of Wi-Fi signals. In addition, the use of deep learning methods for CSI-based activity recognition is still in its infancy. In this paper, we propose a scheme of activity recognition using the joint CSI of the 2.4G frequency band and the 5G frequency band (ARJF), which takes advantage of a novel convolutional neural network (CNN) to automatically extract deep features from the CSI data, to realize the detection and classification of 7 actions. Compared with the recognition result of a single frequency band, the proposed method has better recognition accuracy.
KW - 2.4G
KW - 5G
KW - activity recognition
KW - CSI
UR - https://www.scopus.com/pages/publications/85124429906
U2 - 10.1109/ICCT52962.2021.9657966
DO - 10.1109/ICCT52962.2021.9657966
M3 - 会议稿件
AN - SCOPUS:85124429906
T3 - International Conference on Communication Technology Proceedings, ICCT
SP - 1266
EP - 1270
BT - 2021 IEEE 21st International Conference on Communication Technology, ICCT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Communication Technology, ICCT 2021
Y2 - 13 October 2021 through 16 October 2021
ER -