A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Practical Reasoning for Expressive Description Logics
LPAR '99 Proceedings of the 6th International Conference on Logic Programming and Automated Reasoning
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields
International Journal of Robotics Research
A Tableau Decision Procedure for $\mathcal{SHOIQ}$
Journal of Automated Reasoning
Multi-Camera Human Activity Monitoring
Journal of Intelligent and Robotic Systems
BehaviorScope: Real-Time Remote Human Monitoring Using Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
Activity Recognition for the Smart Hospital
IEEE Intelligent Systems
Accurate activity recognition in a home setting
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
An ontology based approach for activity recognition from video
MM '08 Proceedings of the 16th ACM international conference on Multimedia
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
Learning situation models in a smart home
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Human activity recognition based on the blob features
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Hypertableau reasoning for description logics
Journal of Artificial Intelligence Research
Scalable recognition of daily activities with wearable sensors
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
Ontology-enabled activity learning and model evolution in smart homes
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
A dynamic sliding window approach for activity recognition
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Unsupervised discovery of structure in activity data using multiple eigenspaces
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
A Knowledge-Driven Approach to Activity Recognition in Smart Homes
IEEE Transactions on Knowledge and Data Engineering
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Ontology-based Activity Recognition Framework and Services
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model.