Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

George Okeyo; Liming Chen; Hui Wang; Roy Sterritt

doi:10.1007/978-3-642-16355-5_8

Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

George Okeyo, Liming Chen, Hui Wang, Roy Sterritt

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

Activity modelling plays a critical role in activity recognition and assistance in smart home based assisted living. Ontology-based activity modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this paper, we propose a novel approach for learning and evolving activity models. The approach uses predefined ”seed” ADL ontologies to identify activities from sensor activation streams. We develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. We illustrate our approach through a scenario that shows how ADL models can be evolved to accommodate new ADL activities and preferences of individual smart home’s inhabitants.

Original language	English
Title of host publication	Unknown Host Publication
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	67-82
Number of pages	6
Volume	6406
DOIs	https://doi.org/10.1007/978-3-642-16355-5_8
Publication status	Published (in print/issue) - Oct 2010
Event	The 7th International Conference on Ubiquitous Intelligence and Computing (UIC 2010) - Xian China Duration: 1 Oct 2010 → …

Conference

Conference	The 7th International Conference on Ubiquitous Intelligence and Computing (UIC 2010)
Period	1/10/10 → …

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title = "Ontology-Enabled Activity Learning and Model Evolution in Smart Homes",

abstract = "Activity modelling plays a critical role in activity recognition and assistance in smart home based assisted living. Ontology-based activity modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this paper, we propose a novel approach for learning and evolving activity models. The approach uses predefined ”seed” ADL ontologies to identify activities from sensor activation streams. We develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. We illustrate our approach through a scenario that shows how ADL models can be evolved to accommodate new ADL activities and preferences of individual smart home{\textquoteright}s inhabitants.",

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TY - GEN

T1 - Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

AU - Okeyo, George

AU - Chen, Liming

AU - Wang, Hui

AU - Sterritt, Roy

PY - 2010/10

Y1 - 2010/10

N2 - Activity modelling plays a critical role in activity recognition and assistance in smart home based assisted living. Ontology-based activity modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this paper, we propose a novel approach for learning and evolving activity models. The approach uses predefined ”seed” ADL ontologies to identify activities from sensor activation streams. We develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. We illustrate our approach through a scenario that shows how ADL models can be evolved to accommodate new ADL activities and preferences of individual smart home’s inhabitants.

AB - Activity modelling plays a critical role in activity recognition and assistance in smart home based assisted living. Ontology-based activity modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this paper, we propose a novel approach for learning and evolving activity models. The approach uses predefined ”seed” ADL ontologies to identify activities from sensor activation streams. We develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. We illustrate our approach through a scenario that shows how ADL models can be evolved to accommodate new ADL activities and preferences of individual smart home’s inhabitants.

U2 - 10.1007/978-3-642-16355-5_8

DO - 10.1007/978-3-642-16355-5_8

M3 - Conference contribution

VL - 6406

SP - 67

EP - 82

BT - Unknown Host Publication

PB - Springer

CY - Berlin, Heidelberg

T2 - The 7th International Conference on Ubiquitous Intelligence and Computing (UIC 2010)

Y2 - 1 October 2010

ER -

Ontology-Enabled Activity Learning and Model Evolution in Smart Homes

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