Evaluation of Control Interfaces for Desktop Virtual Environments

Tracking and analyzing the movement trajectories of individuals and groups is an important problem with applications in crowd management and the development of transportation systems. However, real-world tracking is limited due to the size of the trackable area and the precision with which a person can be tracked. Experiments in virtual environments have many advantages, including practically unlimited sizes and the precise measurement of spatial behavior. However, the generalizability of research using virtual environments to real-world scenarios is often limited by the translation of participants’ movements to those of their avatars. We compared human movement patterns in virtual environments with different control interfaces: a handheld joystick, a mouse-and-keyboard setup, and a keyboard-only setup. With each of these controls, participants completed several movement-related tasks of varying difficulty in a limited amount of time. Questionnaires indicated that participants preferred the mouse-and-keyboard setup over the other two setups. Standard performance measures suggested that the joystick underperformed in a variety of tasks. Movement trajectories in the final task indicated that each of the control setups produced somewhat realistic behavior, despite some apparent differences from real-world trajectories. Overall, the results indicated that, given limited resources, mouse-and-keyboard setups consistently outperform joysticks and produce realistic movement patterns.

[1]  Mansour A. Karkoub,et al.  Haptic Direct-Drive Robot Control Scheme in Virtual Reality , 2002, J. Intell. Robotic Syst..

[2]  Dirk Helbing,et al.  Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data , 2007, Adv. Complex Syst..

[3]  Avi Parush,et al.  Cybersickness induced by desktop virtual reality , 2012, Graphics Interface.

[4]  Heinrich H. Bülthoff,et al.  Learning to walk in virtual reality , 2013, TAP.

[5]  Julien Pettré,et al.  The Conference in Pedestrian and Evacuation Dynamics 2014 ( PED 2014 ) A Virtual Reality Platform to Study Crowd Behaviors , 2014 .

[6]  Norman I. Badler,et al.  ADAPT: The Agent Developmentand Prototyping Testbed , 2013, IEEE Transactions on Visualization and Computer Graphics.

[7]  L. Wasserman,et al.  Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .

[8]  J. Loomis,et al.  Immersive virtual environment technology as a basic research tool in psychology , 1999, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[9]  Francisco Rebelo,et al.  Comparing two types of navigational interfaces for Virtual Reality. , 2012, Work.

[10]  David Waller,et al.  WeaVR: a self-contained and wearable immersive virtual environment simulation system , 2015, Behavior research methods.

[11]  Pascal Savard,et al.  A comparative study of four input devices for desktop virtual walkthroughs , 2011, Comput. Hum. Behav..

[12]  George Chryssolouris,et al.  An efficient approach to human motion modeling for the verification of human-centric product design and manufacturing in virtual environments , 2007 .

[13]  Dirk Helbing,et al.  Experimental study of the behavioural mechanisms underlying self-organization in human crowds , 2009, Proceedings of the Royal Society B: Biological Sciences.

[14]  Timothy P. McNamara,et al.  Do We Need to Walk for Effective Virtual Reality Navigation? Physical Rotations Alone May Suffice , 2010, Spatial Cognition.

[15]  Simon Farrell,et al.  Computational Modeling in Cognition: Principles and Practice , 2010 .

[16]  Michel Bierlaire,et al.  Specification, estimation and validation of a pedestrian walking behaviour model , 2007 .

[17]  J. Pettré,et al.  Minimal predicted distance: a common metric for collision avoidance during pairwise interactions between walkers. , 2012, Gait & posture.

[18]  N. Badler,et al.  7-2014 ADAPT : The Agent Development and Prototyping Testbed , 2016 .