Human Activity Monitoring Based on Hidden Markov Models Using a Smartphone

被引：22

作者：

San-Segundo, Ruben

David Echeverry-Correa, Julian ^{[1
]}

Salamea, Christian ^{[2
]}

Manuel Pardo, Jose

机构：

[1] Univ Tecnol Pereira, Dept Elect Engn, Pereira, Risaralda, Colombia

[2] Univ Politecn Madrid, Speech Technol Grp, E-28040 Madrid, Spain

来源：

IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE | 2016年 / 19卷 / 06期

关键词：

ACTIVITY RECOGNITION;

D O I：

10.1109/MIM.2016.7777649

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Human Sensing (HS) is a research area that has received a lot of attention in the last five years due to the high number of promising applications and the increasing interest shown by government and commercial organizations [1]. Based on this interest, several approaches have been proposed in the literature for recognition of human activities applied to different application domains such as healthcare, smart homes [2], ubiquitous computing, ambient assisted living, surveillance, security, and manufacturing [3]. © 1998-2012 IEEE.

引用

页码：27 / 31

页数：5

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