Using fuzzy hidden Markov models for online training evaluation and classification in virtual reality simulators
The goal of the training evaluation in a simulation is to provide feedback about the user performance in the training environment. Approaches to online or offline evaluation of training in simulators based on virtual reality have been proposed. An online evaluator must have low complexity algorithm to not compromise the performance of the simulator. A new approach to quality of training online evaluation in virtual reality worlds is proposed in this work. This approach uses fuzzy hidden Markov models (FHMM) for modeling and classification of trainee in pre-defined classes of training.
fuzzy hidden markov models online training evaluation virtual reality simulators training classification fuzzy statistics