Maintaining knowledge about temporal intervals
Communications of the ACM
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Improving the recognition of interleaved activities
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Using Event Calculus for Behaviour Reasoning and Assistance in a Smart Home
ICOST '08 Proceedings of the 6th international conference on Smart Homes and Health Telematics
Web Semantics: Science, Services and Agents on the World Wide Web
A Reusable Ontology for Fluents in OWL
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
OWL rules: A proposal and prototype implementation
Web Semantics: Science, Services and Agents on the World Wide Web
A Pattern Mining Approach to Sensor-Based Human Activity Recognition
IEEE Transactions on Knowledge and Data Engineering
Pervasive and Mobile Computing
Recognizing Concurrent and Interleaved Activities in Social Interactions
DASC '11 Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing
A Knowledge-Driven Approach to Activity Recognition in Smart Homes
IEEE Transactions on Knowledge and Data Engineering
A Hybrid Ontological and Temporal Approach for Composite Activity Modelling
TRUSTCOM '12 Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications
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Knowledge-driven activity recognition has recently attracted increasing attention but mainly focused on simple activities. This paper extends previous work to introduce a knowledge-driven approach to recognition of composite activities such as interleaved and concurrent activities. The approach combines ontological and temporal knowledge modelling formalisms for composite activity modelling. It exploits ontological reasoning for simple activity recognition and rule-based temporal inference to support composite activity recognition. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. The initial experimental results have shown that average recognition accuracy for simple and composite activities is 100% and 88.26%, respectively.