Petri nets: an introduction
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
Gestures as Input: Neuroelectric Joysticks and Keyboards
IEEE Pervasive Computing
EMG-Based Motion Discrimination Using a Novel Recurrent Neural Network
Journal of Intelligent Information Systems
Prediction of Human Driving Behavior Using Dynamic Bayesian Networks
IEICE - Transactions on Information and Systems
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An adaptive prequential learning framework for bayesian network classifiers
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A log-linearized Gaussian mixture network and its application toEEG pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Online motion recognition using an accelerometer in a mobile device
Expert Systems with Applications: An International Journal
Probabilistic Information Structure of Human Walking
Journal of Medical Systems
Journal of Intelligent and Robotic Systems
Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor
International Journal of Cognitive Informatics and Natural Intelligence
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In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control prosthetic devices or human-assisting manipulators. This paper proposes a task model using a Bayesian network (BN) for motion prediction. Given information of the previous motion, this task model is able to predict occurrence probabilities of the motions concerned in the task. Furthermore, a hybrid motion classification framework has been developed based on the BN motion prediction. Besides the motion prediction, electromyogram (EMG) signals are simultaneously classified by a probabilistic neural network (NN). Then, the motion occurrence probabilities are combined with the NN classifier's outputs to generate motion commands for control. With the proposed motion classification framework, it is expected that classification performance can be enhanced so that motion commands can be more robust and reliable. Experiments have been conducted with four subjects to demonstrate the feasibility of the proposed methods. In these experiments, forearm motions are classified with EMG signals considering a cooking task. Finally, robot manipulation experiments were carried. out to verify the proposed human interface system with a task of taking meal. The experimental results indicate that the proposed methods improved the robustness and stability of motion classification.