Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Advanced Interaction in Context
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
Real-time Analysis of Data from Many Sensors with Neural Networks
ISWC '01 Proceedings of the 5th IEEE International Symposium on Wearable Computers
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
InSense: Interest-Based Life Logging
IEEE MultiMedia
A framework of energy efficient mobile sensing for automatic user state recognition
Proceedings of the 7th international conference on Mobile systems, applications, and services
Landmark detection from mobile life log using a modular Bayesian network model
Expert Systems with Applications: An International Journal
Supporting pervasive computing applications with active context fusion and semantic context delivery
Pervasive and Mobile Computing
An interactive tool based on priority semantic networks
Knowledge-Based Systems
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 | 0.00 |
Multiple sensor-based context inference systems can perceive users' tasks in detail while it requires complicated recognition models with larger resources. Such limitations make the systems difficult to be used for the mobile environment where the context-awareness would be most needed. In order to design and operate the complex models efficiently, this paper proposes an evolutionary process for generating the context models and a selective inference method. Dynamic Bayesian networks are employed as the context models to cope with the uncertain and noisy time-series sensor data, where the operations are managed by using the semantic network which describes the hierarchical and semantic relations of the contexts. The proposed method was validated on a wearable system with variable sensors including accelerometers, gyroscopes, physiological sensors, and data gloves.