Comparing VE locomotion interfaces

To compare and evaluate locomotion interfaces for users who are (virtually) moving on foot in VEs, we performed a study to characterize task behavior and task performance with different visual and locomotion interfaces. In both a computer-generated environment and a corresponding real environment, study participants walked to targets on walls and stopped as close to them as they could without making contact. In each of five experimental conditions participants used a combination of one of three locomotion interfaces (really walking, walking-in-place, and joystick flying), and one of three visual conditions (head-mounted display, unrestricted natural vision, or field-of-view-restricted natural vision). We identified metrics and collected data that captured task performance and the underlying kinematics of the task. Our results show: 1) Over 95% of the variance in simple motion paths is captured in three critical values: peak velocity; when, in the course of a motion, the peak velocity occurs; and peak deceleration. 2) Correlations of those critical value data for the conditions taken pairwise suggest a coarse ordering of locomotion interfaces by "naturalness." 3) Task performance varies with interface condition, but correlations of that value for conditions taken pairwise do not cluster by naturalness. 4) The perceptual variable, r (also known as the time-to-contact) calculated at the point of peak deceleration has higher correlation with task performance than r calculated at peak velocity.

[1]  James Templeman,et al.  PERFORMANCE BASED DESIGN OF A NEW VIRTUAL LOCOMOTION CONTROL , 1997 .

[2]  David N. Lee,et al.  Plummeting gannets: a paradigm of ecological optics , 1981, Nature.

[3]  Sabarish V. Babu,et al.  Effects of travel technique on cognition in virtual environments , 2004, IEEE Virtual Reality 2004.

[4]  Andrea H. Mason,et al.  Reaching movements to augmented and graphic objects in virtual environments , 2001, CHI.

[5]  J. A. Boldovici,et al.  The Elements of Training Evaluation , 2002 .

[6]  C. Osgood The similarity paradox in human learning; a resolution. , 1949, Psychological review.

[7]  Gregor Schöner,et al.  Dynamic theory of action-perception patterns: The time-before-contact paradigm , 1994 .

[8]  P. Foo,et al.  Functional stabilization of unstable fixed points: human pole balancing using time-to-balance information. , 2000, Journal of experimental psychology. Human perception and performance.

[9]  Hiroo Iwata,et al.  Path Reproduction Tests Using a Torus Treadmill , 1999, Presence.

[10]  Terry Allard,et al.  Spatial Orientation and Wayfinding in Large-Scale Virtual Spaces. , 1999 .

[11]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[12]  Jack M. Loomis,et al.  Locomotion Mode Affects the Updating of Objects Encountered During Travel: The Contribution of Vestibular and Proprioceptive Inputs to Path Integration , 1998, Presence.

[13]  Mary C. Whitton,et al.  Walking > walking-in-place > flying, in virtual environments , 1999, SIGGRAPH.

[14]  Wallace J. Sadowski,et al.  VE-Based Training Strategies for Acquiring Survey Knowledge , 2002, Presence: Teleoperators & Virtual Environments.

[15]  Stuart C. Grant,et al.  Contributions of Proprioception to Navigation in Virtual Environments , 1998, Hum. Factors.

[16]  David N. Lee,et al.  A Theory of Visual Control of Braking Based on Information about Time-to-Collision , 1976, Perception.

[17]  E. Reed The Ecological Approach to Visual Perception , 1989 .

[18]  David N. Lee 16 Visuo-Motor Coordination in Space-Time , 1980 .

[19]  M. Turvey,et al.  The ecological approach to perceiving-acting: a pictorial essay. , 1986, Acta psychologica.

[20]  Mel Slater,et al.  Taking steps: the influence of a walking technique on presence in virtual reality , 1995, TCHI.

[21]  M. Mon-Williams,et al.  Motor Control and Learning , 2006 .