Open Access
ARTICLE
Human Movement Detection and Gait Periodicity Analysis via Channel State Information
Wenyuan Liu1,2, Zijuan Liu1,*, Lin Wang1, Binbin Li1, Nan Jing1
1 School of Information Science and Engineering, Yanshan University
2 The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province, School of Economics and Management, Yanshan University, Qinhuangdao, HeBei, China
E-mail: liuzijuan100@163.com, wlin@ysu.edu.cn
* Corresponding Author: Zijuan Liu,
Computer Systems Science and Engineering 2018, 33(2), 137-147. https://doi.org/10.32604/csse.2018.33.137
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%.
Keywords
Cite This Article
W. Liu, Z. Liu, L. Wang, B. Li and N. Jing, "Human movement detection and gait periodicity analysis via channel state information,"
Computer Systems Science and Engineering, vol. 33, no.2, pp. 137–147, 2018. https://doi.org/10.32604/csse.2018.33.137
Citations