Feature Sub-set Selection for Activity Recognition

Francisco J. Quesada, Francisco Moya, Macarena Espinilla, Luis Martínez, Chris D. Nugent

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The delivery of Ambient Assisted Living services, specifically relating to the smart-home paradigm, assumes that people can be provided with help, automatically and in real time, in their homes as and when required. Nevertheless, the deployment of a smart-home can lead to high levels of expense due to configuration requirements of multiple sensing and actuating technology. In addition, the vast amount of data produced leads to increased levels of computational complexity when trying to ascertain the underlying behavior of the inhabitant. This contribution presents a methodology based on feature selection which aims to reduce the number of sensors required whilst still maintaining acceptable levels of activity recognition performance. To do so, a smart-home dataset has been utilized, obtaining a configuration of sensors with the half sensors with respect to the original configuration.
Original languageEnglish
Title of host publicationInclusive Smart Cities and e-Health
Subtitle of host publication13th International Conference on Smart Homes and Health Telematics, ICOST 2015, Geneva, Switzerland, June 10-12, 2015, Proceedings
EditorsAntoine Geissbühler, Jacques Demongeot, Mounir Mokhtari, Bessam Abdulrazak, Hamdi Aloulou
Place of PublicationCham
PublisherSpringer International Publishing
Pages307-312
Number of pages6
ISBN (Electronic)978-3-319-19312-0
ISBN (Print)978-3-319-19311-3
DOIs
Publication statusPublished - 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
Volume9102
ISSN (Print)0302-9743

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