TY - JOUR
T1 - Wii
T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017
AU - Lv, Jiguang
AU - Yang, Wu
AU - Man, Dapeng
AU - Du, Xiaojiang
AU - Yu, Miao
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - Human behavior data is the basis of behavior analysis, and usually we need to collect large quantities of data before analysis. Most existing data collection methods are labor intensive works in which the volunteers need to be asked to behave naturally under the monitoring of researchers. Identity identification can be used in passive data collection of human behavior analysis systems in big data. Previous researches show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human's gait is unique from each other like fingerprint and iris. As a result, researchers start to explore the ability of WiFi in human identification. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human's gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract entities' gait features from both time and frequency domain. Based on these features, Wii realizes identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated in a typical indoor scenario. The results indicate that Wii achieves high identification accuracy with low computational cost and has the potential to work in human behavior analysis systems.
AB - Human behavior data is the basis of behavior analysis, and usually we need to collect large quantities of data before analysis. Most existing data collection methods are labor intensive works in which the volunteers need to be asked to behave naturally under the monitoring of researchers. Identity identification can be used in passive data collection of human behavior analysis systems in big data. Previous researches show the sensing potential of WiFi signals in a device-free passive manner. It is confirmed that human's gait is unique from each other like fingerprint and iris. As a result, researchers start to explore the ability of WiFi in human identification. However, the identification accuracy of existing approaches is not satisfactory in practice. In this paper, we present Wii, a device-free WiFi-based Identity Identification approach utilizing human's gait based on Channel State Information (CSI) of WiFi signals. Principle Component Analysis (PCA) and low pass filter are applied to remove the noises in the signals. We then extract entities' gait features from both time and frequency domain. Based on these features, Wii realizes identity identification through a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. It is implemented using commercial WiFi devices and evaluated in a typical indoor scenario. The results indicate that Wii achieves high identification accuracy with low computational cost and has the potential to work in human behavior analysis systems.
KW - channel state information
KW - human behavior analysis
KW - human gait
KW - human identification
KW - WiFi
UR - http://www.scopus.com/inward/record.url?scp=85046372175&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046372175&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2017.8254429
DO - 10.1109/GLOCOM.2017.8254429
M3 - Conference article
AN - SCOPUS:85046372175
SN - 2334-0983
VL - 2018-January
SP - 1
EP - 6
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
Y2 - 4 December 2017 through 8 December 2017
ER -