In this research, computational intelligence techniques are applied towards the modeling of human sensations in virtual environments. We specifically focus on the following important questions: (1) how to efficiently model the relationship between human sensations and the physical stimuli presented to humans, (2) how to validate the human sensation models, and (3) how to reduce the size of the input data when it gets large and how to select the information which is most important to human sensation modeling. In order to provide an experimental testbed for the implementation of the proposed learning and analysis techniques, a full-body motion virtual reality interface capable of recording human sensations is developed. We propose using cascade neural networks with node-decoupled extended Kalman filter training for modeling human sensation in virtual environments. For the purpose of sensation model validation, we propose using a stochastic similarity measure based on hidden Markov models to calculate the relative similarity between model-generated sensation and actual human sensation. Next, we investigate a number of feature extraction and input selection techniques for reducing the input data size in human sensation modeling. We propose and develop a new input selection method based on independent component analysis, which is capable of reducing the data size and selecting the stimuli information that is most important to the human sensation.
Anticipating Human Intention for Full-Body Motion Prediction in Object Grasping and Placing Tasks
