Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Bayesian approach to sensor-based context awareness
Personal and Ubiquitous Computing
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
MyLifeBits: a personal database for everything
Communications of the ACM - Personal information management
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
Modular Bayesian Network Learning for Mobile Life Understanding
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
COSAR: hybrid reasoning for context-aware activity recognition
Personal and Ubiquitous Computing
Motion- and location-based online human daily activity recognition
Pervasive and Mobile Computing
Activity recognition based on RFID object usage for smart mobile devices
Journal of Computer Science and Technology
Accurate Activity Recognition Using a Mobile Phone Regardless of Device Orientation and Location
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
Semi-Markov conditional random fields for accelerometer-based activity recognition
Applied Intelligence
Inference in multiply sectioned Bayesian networks with extended Shafer-Shenoy and lazy propagation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Incremental compilation of bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Issues and requirements for bayesian approaches in context aware systems
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Maximal prime subgraph decomposition of Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Context-aware semantic discovery for next generation mobile systems
IEEE Communications Magazine
Centinela: A human activity recognition system based on acceleration and vital sign data
Pervasive and Mobile Computing
| Hi-index | 12.05 |
Recently, mobile context inference becomes an important issue. Bayesian probabilistic model is one of the most popular probabilistic approaches for context inference. It efficiently represents and exploits the conditional independence of propositions. However, there are some limitations for probabilistic context inference in mobile devices. Mobile devices relatively lacks of sufficient memory. In this paper, we present a novel method for efficient Bayesian inference on a mobile phone. In order to overcome the constraints of the mobile environment, the method uses two-layered Bayesian networks with tree structure. In contrast to the conventional techniques, this method attempts to use probabilistic models with fixed tree structures and intermediate nodes. It can reduce the inference time by eliminating junction tree creation. To evaluate the performance of this method, an experiment is conducted with data collected over a month. The result shows the efficiency and effectiveness of the proposed method.