Elderly care: activities of daily living classification with an S band radar

被引：4

作者：

Shrestha, Aman ^{[1
]}

Le Kernec, Julien ^{[1
,2
]}

Fioranelli, Francesco ^{[1
]}

Lin, Yier ^{[2
]}

He, Qian ^{[2
]}

Lorandel, Jordane ^{[3
]}

Romain, Olivier ^{[3
]}

机构：

[1] Univ Glasgow, Sch Engn, Commun Sensing & Imaging Grp, Glasgow, Lanark, Scotland

[2] Univ Elect Sci & Technol China, Sch Informat & Elect, Chengdu, Sichuan, Peoples R China

[3] Univ Cergy Pontoise, ETIS ASTRE, Cergy Pontoise, France

来源：

JOURNAL OF ENGINEERING-JOE | 2019年 / 2019卷 / 21期

基金：

英国工程与自然科学研究理事会;

关键词：

geriatrics; pattern classification; data privacy; health care; radar; feature selection; elderly care; daily living classification; privacy; activity detection; human movement classification; S band radar; FALL DETECTION; RECOGNITION; SENSORS;

D O I：

10.1049/joe.2019.0561

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Falls in the elderly represent a serious challenge for the global population. To address it, monitoring of daily living has been suggested, with radar emerging to be a useful platform for it due to its various benefits with acceptance and privacy. Here, we show results from the use of an S band radar for activity detection and the importance of selecting specific frequency bins to improve its suitability for human movement classification. The use of feature selection to improve detection of key activities such as falls has been presented. Initial results of 65% are improved to 85% and further to 90% with the aforementioned methods.

引用

页码：7601 / 7606

页数：6

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