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2017 | OriginalPaper | Chapter

Improving Activity Classification Using Ontologies to Expand Features in Smart Environments

Authors : Alberto Salguero, Macarena Espinilla

Published in: Ubiquitous Computing and Ambient Intelligence

Publisher: Springer International Publishing

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Abstract

Activity recognition is a promising field of research aiming to develop solutions within smart environments to provide relevant solutions on ambient assisted living, among others. The process of activity recognition aims to recognize the actions and goals of one or more person in a environment with a set of sensors are deployed, basing on the sensor data stream that capture a series of observations of actions and environmental conditions. This contributions presents the initial results from a new methodology that considers the use of ontologies to expand the set of feature vector, which is computed by using the sensor data stream, that is used in the process of activity recognition by data-driven approaches. The obtained results indicates that the use of extended feature vectors provided by the use of ontology offers a better accuracy regarding the original feature vectors used in the process of activity recognition with different data-driven approaches.

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previous chapter Impact of Missing Clinical Data for the Monitoring of Patients with Chronic Diseases
next chapter Inter-activity Behaviour Modelling Using Long Short-Term Memory Networks
Footnotes
1
 
2
For simplicity, only the activation of the sensors has been taken into account for the experiment in Sect. 4.2. However, it is also possible to consider the deactivations of the sensors by just enabling a flag in the application developed for loading the dataset in the ontology.
 
3
The Weka implementation of the C4.5 classifier is called J48.
 
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Metadata
Title
Improving Activity Classification Using Ontologies to Expand Features in Smart Environments
Authors
Alberto Salguero
Macarena Espinilla
Copyright Year
2017
Book
Ubiquitous Computing and Ambient Intelligence

Print ISBN: 978-3-319-67584-8

Electronic ISBN: 978-3-319-67585-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-67585-5_40

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