Human sensation modeling in virtual environments

This paper aims to study human-machine integration in the human sensation aspect. We propose using cascade neural networks to model human sensation during the interaction, between humans and machines. The fidelity of the sensation models is verified using a hidden Markov model (HMM)-based similarity measure scheme. We applied this modeling technique in a full-body motion virtual reality interface-“motion-based movie”. The sensation levels of the human participants in this application were modeled effectively by the cascade neural networks and the fidelity of the models were revealed by the HMM similarity measure scheme