Fall detection with multi-domain features by a portable FMCW radar

Chuanwei Ding; Yu Zou; Li Sun; Hong Hong; Xiaohua Zhu; Changzhi Li

doi:10.1109/IEEE-IWS.2019.8804036

Fall detection with multi-domain features by a portable FMCW radar

Chuanwei Ding, Yu Zou, Li Sun, Hong Hong, Xiaohua Zhu, Changzhi Li

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

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.

Original language	English
Title of host publication	2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728107165
DOIs	https://doi.org/10.1109/IEEE-IWS.2019.8804036
State	Published - May 2019
Event	2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Guangzhou, China Duration: May 19 2019 → May 22 2019

Publication series

Name	2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings

Conference

Conference	2019 IEEE MTT-S International Wireless Symposium, IWS 2019
Country	China
City	Guangzhou
Period	05/19/19 → 05/22/19

Keywords

DRDT
FMCW
fall detection
machine learning
multi-domain

Access to Document

10.1109/IEEE-IWS.2019.8804036

Link to publication in Scopus

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Cite this

Ding, C., Zou, Y., Sun, L., Hong, H., Zhu, X., & Li, C. (2019). Fall detection with multi-domain features by a portable FMCW radar. In 2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings [8804036] (2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEEE-IWS.2019.8804036

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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",

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language = "English",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Ding, C, Zou, Y, Sun, L, Hong, H, Zhu, X & Li, C 2019, Fall detection with multi-domain features by a portable FMCW radar. in 2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings., 8804036, 2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2019 IEEE MTT-S International Wireless Symposium, IWS 2019, Guangzhou, China, 05/19/19. https://doi.org/10.1109/IEEE-IWS.2019.8804036

Fall detection with multi-domain features by a portable FMCW radar. / Ding, Chuanwei; Zou, Yu; Sun, Li; Hong, Hong; Zhu, Xiaohua; Li, Changzhi.

2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8804036 (2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Ding C, Zou Y, Sun L, Hong H, Zhu X, Li C. Fall detection with multi-domain features by a portable FMCW radar. In 2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8804036. (2019 IEEE MTT-S International Wireless Symposium, IWS 2019 - Proceedings). https://doi.org/10.1109/IEEE-IWS.2019.8804036