@inproceedings{d47734a5b5c74ccbbc66624dac2be34e,
title = "Fall detection with multi-domain features by a portable FMCW radar",
abstract = "Fall detection is important for senior care. In order to classify fall and other fall-similar daily motions, a novel dynamic range-Doppler trajectory (DRDT) method based on a frequency-modulated continuous-wave (FMCW) radar system is proposed. Multi-domain features including temporal changes of range, Doppler, radar cross-section (RCS) and dispersion are extracted from echo signals for a subspace K-Nearest Neighbor (KNN) machine learning classifier. Extensive experiments demonstrated its feasibility and an average accuracy of 95.5% was achieved in recognizing six typical fall-similar motions.",
keywords = "DRDT, FMCW, fall detection, machine learning, multi-domain",
author = "Chuanwei Ding and Yu Zou and Li Sun and Hong Hong and Xiaohua Zhu and Changzhi Li",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; null ; Conference date: 19-05-2019 Through 22-05-2019",
year = "2019",
month = may,
doi = "10.1109/IEEE-IWS.2019.8804036",
language = "English",
series = "2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings",
}