TY - GEN
T1 - Human Activity Recognition based on Collaboration of Vision and WiFi Signals
AU - Li, Shibo
AU - Ge, Yao
AU - Shentu, Minjian
AU - Zhu, Shuyuan
AU - Imran, Muhammad
AU - Abbasi, Qammer
AU - Cooper, Jonathan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In the WiFi protocol, channel state information (CSI) is the modulated as the fine-grained data to assess the channel efficiency. Meanwhile, it contains the information about the environment change, including the movement of human in a specific environment. Therefore, the CSI data can be used to recognize the human activity. In this paper, we design a vision and WiFi collaboration-based human activity recognition scheme to classify the human activities. More specifically, we collect the CSI data from the WiFi signals and the human skeleton points from the video signals. Then, we construct a long-short-term Transformer network to build up the collaboration of the CSI data and the skeleton points. Based on this collaboration, we can use the CSI data to well recognize the human activities.
AB - In the WiFi protocol, channel state information (CSI) is the modulated as the fine-grained data to assess the channel efficiency. Meanwhile, it contains the information about the environment change, including the movement of human in a specific environment. Therefore, the CSI data can be used to recognize the human activity. In this paper, we design a vision and WiFi collaboration-based human activity recognition scheme to classify the human activities. More specifically, we collect the CSI data from the WiFi signals and the human skeleton points from the video signals. Then, we construct a long-short-term Transformer network to build up the collaboration of the CSI data and the skeleton points. Based on this collaboration, we can use the CSI data to well recognize the human activities.
KW - Transformer
KW - WiFi
KW - channel state information
KW - human activity recognition
KW - long-short-term
UR - https://www.scopus.com/pages/publications/85125312667
U2 - 10.1109/UCET54125.2021.9674970
DO - 10.1109/UCET54125.2021.9674970
M3 - Conference contribution
AN - SCOPUS:85125312667
T3 - 2021 6th International Conference on UK-China Emerging Technologies, UCET 2021
SP - 204
EP - 208
BT - 2021 6th International Conference on UK-China Emerging Technologies, UCET 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on UK-China Emerging Technologies, UCET 2021
Y2 - 4 November 2021 through 6 November 2021
ER -