@Article{csse.2018.33.137,
AUTHOR = {Wenyuan Liu, Zijuan Liu, Lin Wang, Binbin Li, Nan Jing},
TITLE = {Human Movement Detection and Gait Periodicity Analysis via Channel State Information},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {33},
YEAR = {2018},
NUMBER = {2},
PAGES = {137--147},
URL = {http://www.techscience.com/csse/v33n2/39965},
ISSN = {},
ABSTRACT = {In recent years, movement detection and gait recognition methods using different techniques emerge in an endless stream. On the one hand, wearable
sensors need be worn by the detecting target and the method based on camera requires line of sight. On the other hand, radio frequency signals are easy to be
impaired. In this paper, we propose a novel multi-layer filter of channel state information (CSI) to capture moving individuals in dynamic environments and
analyze his/her gait periodicity. We design and evaluate an efficient CSI subcarrier feature difference to the multi-layer filtering method leveraging principal
component analysis (PCA) and discrete wavelet transform (DWT) to eliminate the noises. Furthermore, we propose the profile matching mechanism for
movement detection and the gait periodicity analysis mechanism for human gait. Experimental results in different environments indicate that our approach
performs identification with an average accuracy of 94%.},
DOI = {10.32604/csse.2018.33.137}
}