TY - GEN
T1 - A new method for indoor non-rhythmic human motions classification using ultra-wideband radar
AU - Ding, Chuanwei
AU - Zhao, Heng
AU - Liao, Zhicheng
AU - Zhang, Li
AU - Li, Changzhi
N1 - Publisher Copyright:
© 2017 Applied Computational Electromagnetics Society.
PY - 2017/9/26
Y1 - 2017/9/26
N2 - In order to classify non-rhythmic human motions by ultra-wideband (UWB) radar, a weighted rang-time-frequency transform (WRTFT) method is proposed to extract both Doppler and range features. And the bootstrap-aggregated decision trees (Bagged Trees) classifier is utilized to recognize each non-rhythmic human motion. Experiments show that the proposed method can achieve 92.9% classification accuracy for recognizing seven typical non-rhythmic activities.
AB - In order to classify non-rhythmic human motions by ultra-wideband (UWB) radar, a weighted rang-time-frequency transform (WRTFT) method is proposed to extract both Doppler and range features. And the bootstrap-aggregated decision trees (Bagged Trees) classifier is utilized to recognize each non-rhythmic human motion. Experiments show that the proposed method can achieve 92.9% classification accuracy for recognizing seven typical non-rhythmic activities.
KW - Bagged Trees
KW - Ultra-wideband radar
KW - non-rhythmic human motion
KW - range-time-frequency analysis
UR - http://www.scopus.com/inward/record.url?scp=85032816806&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85032816806
T3 - 2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017
BT - 2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2017
Y2 - 1 August 2017 through 4 August 2017
ER -