Multioccupant Activity Recognition in Pervasive Smart Home Environments

被引：86

作者：

Benmansour, Asma ^{[1
]}

Bouchachia, Abdelhamid ^{[2
]}

Feham, Mohammed ^{[1
]}

机构：

[1] Univ Tlemcen, STIC Lab, Tilimsen 13000, Algeria

[2] Bournemouth Univ, Poole BH12 5BB, Dorset, England

来源：

ACM COMPUTING SURVEYS | 2015年 / 48卷 / 03期

关键词：

Theory; Design; Performance; Human activity recognition; smart homes; pervasive environments; multiple occupancy; MODELS; HEALTH;

D O I：

10.1145/2835372

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Human activity recognition in ambient intelligent environments like homes, offices, and classrooms has been the center of a lot of research for many years now. The aim is to recognize the sequence of actions by a specific person using sensor readings. Most of the research has been devoted to activity recognition of single occupants in the environment. However, living environments are usually inhabited by more than one person and possibly with pets. Hence, human activity recognition in the context of multioccupancy is more general, but also more challenging. The difficulty comes from mainly two aspects: resident identification, known as data association, and diversity of human activities. The present survey article provides an overview of existing approaches and current practices for activity recognition in multioccupant smart homes. It presents the latest developments and highlights the open issues in this field.

引用

页数：36

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