Open AccessIndoor non‐rhythmic human motion classification using a frequency‐modulated continuous‐wave radar
Author(s) -
Zou Yu,
Ding Chuanwei,
Hong Hong,
Li Changzhi,
Zhu Xiaohua
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0560
Subject(s) - radar , computer science , artificial intelligence , rhythm , subspace topology , classifier (uml) , echo (communications protocol) , pattern recognition (psychology) , doppler frequency , human motion , motion (physics) , computer vision , doppler effect , acoustics , telecommunications , physics , computer network , astronomy
Human motion classification is widely used in intelligent house, surveillance, search and rescue operation, intelligent house, and elder monitoring. In this study, a frequency‐modulated continuous‐wave radar is utilised to classify non‐rhythmic human motion in an indoor scenario. Both the range and Doppler features are extracted from echo signals for a machine learning classifier subspace K ‐nearest neighbour. Extensive experiments demonstrate its feasibility, and an accuracy rate of 94.2% was achieved in recognition of eight typical non‐rhythmic motions.