Ontology-enabled activity learning and model evolution in smart homes

Authors:
George Okeyo;Liming Chen;Hui Wang;Roy Sterritt
Affiliations:
Computer Science Research Institute, School of Computing and Mathematics, University of Ulster, Newtownabbey, United Kingdom;Computer Science Research Institute, School of Computing and Mathematics, University of Ulster, Newtownabbey, United Kingdom;Computer Science Research Institute, School of Computing and Mathematics, University of Ulster, Newtownabbey, United Kingdom;Computer Science Research Institute, School of Computing and Mathematics, University of Ulster, Newtownabbey, United Kingdom
Venue:
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Year:
2010

Citing 11
Cited 2

Measuring Similarity between Ontologies

EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Practical Reasoning for Expressive Description Logics

LPAR '99 Proceedings of the 6th International Conference on Logic Programming and Automated Reasoning
Clustering Ontology-Based Metadata in the Semantic Web

PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage

ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Living assistance systems: an ambient intelligence approach

Proceedings of the 28th international conference on Software engineering
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields

International Journal of Robotics Research
Ontology change: Classification and survey

The Knowledge Engineering Review
An ontology based approach for activity recognition from video

MM '08 Proceedings of the 16th ACM international conference on Multimedia
Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor

ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Scalable recognition of daily activities with wearable sensors

LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Unsupervised discovery of structure in activity data using multiple eigenspaces

LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness

A survey on ontologies for human behavior recognition

ACM Computing Surveys (CSUR)
Dynamic sensor data segmentation for real-time knowledge-driven activity recognition

Pervasive and Mobile Computing

Quantified Score

Hi-index	0.00

Visualization

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.