Sensor fusion integrating contextual information
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Advanced Interaction in Context
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Evolving dynamic Bayesian networks with Multi-objective genetic algorithms
Applied Intelligence
Gesture spotting with body-worn inertial sensors to detect user activities
Pattern Recognition
Context-aware systems: A literature review and classification
Expert Systems with Applications: An International Journal
A framework of energy efficient mobile sensing for automatic user state recognition
Proceedings of the 7th international conference on Mobile systems, applications, and services
Supporting pervasive computing applications with active context fusion and semantic context delivery
Pervasive and Mobile Computing
ConaMSN: A context-aware messenger using dynamic Bayesian networks with wearable sensors
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine
| Hi-index | 12.05 |
Mobile devices can perceive greater details of user states with the increasing integration of mobile sensors into a pervasive computing framework, yet they consume large amounts of batteries and computational resources. This paper proposes a semantic management method which efficiently integrates multiple contexts into the mobile system by analyzing the semantic hierarchy and temporal relations. The proposed method semantically decides the recognition order of the contexts and identifies each context using a corresponding dynamic Bayesian network (DBN). To sort out the contexts, we designed a semantic network using a knowledge-driven approach, whereas DBNs are constructed with a data-driven approach. The proposed method was validated on a pervasive computing framework, which included multiple mobile sensors (such as motion sensors, data-gloves, and bio-signal sensors). Experimental results showed that the semantic management of multiple contexts dramatically reduced the recognition cost.