Human Motion Serialization Recognition With Through-the-Wall Radar

Xiaqing Yang; Pengyun Chen; Mingyang Wang; Shisheng Guo; Chao Jia; Guolong Cui

doi:10.1109/ACCESS.2020.3029247

IEEE Access (Jan 2020)

Human Motion Serialization Recognition With Through-the-Wall Radar

Xiaqing Yang,
Pengyun Chen,
Mingyang Wang,
Shisheng Guo,
Chao Jia,
Guolong Cui

Affiliations

Xiaqing Yang: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Pengyun Chen: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Mingyang Wang: ORCiD; Southwest Institute of Electronic Technology, Chengdu, China
Shisheng Guo: ORCiD; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Chao Jia: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Guolong Cui: ORCiD; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

DOI: https://doi.org/10.1109/ACCESS.2020.3029247
Journal volume & issue: Vol. 8
pp. 186879 – 186889

Abstract

Read online

Motivated by the intrinsic dynamics of physical motion as well as establishment of target motion model, this article addresses the problem of human motion recognition with ultra wide band (UWB) through-the-wall radar (TWR) in a novel view of range profile serialization. Specifically, we first convert the original radar echoes into range profiles. Then, an auto-encoder network (AEN) with three dense layers is adopted to reduce the dimension and extract the features of each range profile. After that, a gated recurrent unit (GRU) network with two hidden layers is employed to deal with the features of each time-range slice and output the recognition results at each slice in real time. Finally, experimental data with respect to four different behind-wall human motions is collected by self-developed UWB TWR to validate the effectiveness of the proposed model. The results show that the proposed model can validly recognize the human motion serialization and achieve 93% recognition accuracy within the initial 20% duration of the activities (the average durations are 4s, 5.5s, 3s and 4.5s), which is of great significance for real-time human motion recognition.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

About the journal

Abstract

Keywords