This paper describes a study to assess the influence of a variety of factors on reported level of presence in immersive virtual environments. It introduces the idea of “stacking depth,” that is, where a participant can simulate the process of entering the virtual environment while already in such an environment, which can be repeated to several levels of depth. An experimental study including 24 subjects was carried out. Half of the subjects were transported between environments by using virtual head-mounted displays, and the other half by going through doors. Three other binary factors were whether or not gravity operated, whether or not the subject experienced a virtual precipice, and whether or not the subject was followed around by a virtual actor. Visual, auditory, and kinesthetic representation systems and egocentric/exocentric perceptual positions were assessed by a preexperiment questionnaire. Presence was assessed by the subjects as their sense of “being there,” the extent to which they experienced the virtual environments as more the presenting reality than the real world in which the experiment was taking place, and the extent to which the subject experienced the virtual environments as places visited rather than images seen. A logistic regression analysis revealed that subjective reporting of presence was significantly positively associated with visual and kinesthetic representation systems, and negatively with the auditory system. This was not surprising since the virtual reality system used was primarily visual. The analysis also showed a significant and positive association with stacking level depth for those who were transported between environments by using the virtual HMD, and a negative association for those who were transported through doors. Finally, four of the subjects moved their real left arm to match movement of the left arm of the virtual body displayed by the system. These four scored significantly higher on the kinesthetic representation system than the remainder of the subjects.

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This paper describes a new measure for presence in immersive virtual environments (VEs) based on data that can be obtained unobtrusively during the course of a VE experience. At different times during an experience a participant will occasionally switch between interpreting the totality of sensory inputs as forming the VE or the real world. The number of transitions from virtual to real is counted, and using some simplifying assumptions, a probabilistic Markov Chain model can be constructed to model these transitions. This can be used to estimate the equilibrium probability of being 'present' in the VE. This technique was applied in the context of an experiment to assess the relationship between presence and body movement in an immersive VE. The movement was that required by subjects to reach out and touch successive pieces on a Tri-Dimensional chess board.The experiment included 20 subjects, 10 of whom had to reach out to touch the chess pieces ('active group'), and the other 10 controls only had to click a hand-held mouse button. The results showed that amongst the active group there was a significant positive association between body movement and presence. The result lends support to interaction paradigms that are based on maximizing the match between sensory data and proprioception.

We describe an experiment to assess the influence of body movements on presence in a virtual environment. In the experiment 20 participants were to walk through a virtual field of trees and count the trees with diseased leaves. A 2 x 2 between subjects design was used to assess the influence of two factors on presence: tree height variation and task complexity. The field with greater variation in tree height required participants to bend down and look up more than in the lower variation tree height field. In the higher complexity task participants were told to remember the distribution of diseased trees in the field as well as to count them. The results showed a significant positive association between reported presence and the amount of body movement in particular, head yaw--and the extent to which participants bent down and stood up. There was also a strong interaction effect between task complexity and gender: Women in the more-complex task reported a much lower sense of presence than in the simpler task. For applications in which presence is an important requirement, the research in this paper suggests that presence will be increased when interaction techniques are employed that permit the user to engage in whole-body movement.

This paper presents an interactive technique for moving through an immersive virtual environment (or "virtual reality"). The technique is suitable for applications where locomotion is restricted to ground level. The technique is derived from the idea that presence in virtual environments may be enhanced the stronger the match between proprioceptive information from human body movements, and sensory feedback from the computer generated displays. The technique is an attempt to simulate body movements associated with walking. The participant "walks in place" to move through the virtual environment across distances greater than the physical limitations imposed by the electro-magnetic tracking devices. A neural network is used to analyse the stream of coordinates from the head-mounted display, to determine whether or not the participant is walking on the spot. Whenever it determines the walking behaviour, the participant is moved through virtual space in the direction of gaze. We discuss two experimental studies to assess the impact on presence of this method in comparison to the usual hand pointing method of navigation in virtual reality. The studies suggest that subjective rating of presence is enhanced by the walking method provided that participants subjectively associate with the virtual body provided in the environment. An application of the technique to climbing steps and ladders is also presented.

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The term big data occurs more frequently now than ever before. A large number of field and subjects, ranging from everyday life to traditional research field (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversifie types, issues, and solutions for big data more than ever be-fore. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.