Characteristics of personal space during obstacle circumvention in physical and virtual environments

Gérin-Lajoie, Martin; Richards, Carol L.; Fung, Joyce; McFadyen, Bradford J.

doi:10.1016/j.gaitpost.2007.03.015

Cited by 124 publications

(94 citation statements)

References 21 publications

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“…They showed some differences in the generated trajectories such as a larger lateral deviation, a larger obstacle clearance as well as a slower walking speed in virtual conditions compared to real ones. Gérin-Lajoie et al [7] considered collision avoidance with a static cylinder. Performed in an HMD environment, they showed that the global shape of the personal space is preserved, but is slightly increased in virtual conditions compared to real ones.…”

Section: Collision Avoidance In Virtual Environmentsmentioning

confidence: 99%

“…However, there are still not clear the causes of why humans consistently keep greater distances from virtual obstacles than from they real counterparts. While Fink et al [5] hypothesize that the differences could be explained by the increased uncertainty about the obstacle egocentric location in IVEs, Gérin-Lajoie et al [7] hypothesize that the differences could be due to limited field of view. HMDs have a limited field of view which restrict the user's view to the virtual environment (VE).…”

Section: Collision Avoidance In Virtual Environmentsmentioning

confidence: 99%

“…For example, Fink. et al [5] used a HMD with a FoV of 60°H and Gérin-Lajoie et al [7] an HDM with a FoV of 40°H. In addition to the limited field of view, collision avoidance behavior is dominated by visual information from the VE, although extraretinal cues and fear to collide with obstacles in the real world often reduce natural locomotion behavior and gait [12].…”

Section: Collision Avoidance In Virtual Environmentsmentioning

confidence: 99%

See 2 more Smart Citations

Virtual proxemics: Locomotion in the presence of obstacles in large immersive projection environments

Argelaguet

Olivier

Bruder

et al. 2015

2015 IEEE Virtual Reality (VR)

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Figure 1: Experimental conditions considered on the collision avoidance experiment. We analyzed the participants' avoidance behavior for real (left) and virtual objects (right), considering anthropomorphic and inanimated objects. ABSTRACTIn this paper, we investigate obstacle avoidance behavior during real walking in a large immersive projection setup. We analyze the walking behavior of users when avoiding real and virtual static obstacles. In order to generalize our study, we consider both anthropomorphic and inanimate objects, each having his virtual and real counterpart. The results showed that users exhibit different locomotion behaviors in the presence of real and virtual obstacles, and in the presence of anthropomorphic and inanimate objects. Precisely, the results showed a decrease of walking speed as well as an increase of the clearance distance (i. e., the minimal distance between the walker and the obstacle) when facing virtual obstacles compared to real ones. Moreover, our results suggest that users act differently due to their perception of the obstacle: users keep more distance when the obstacle is anthropomorphic compared to an inanimate object and when the orientation of anthropomorphic obstacle is from the profile compared to a front position. We discuss implications on future large shared immersive projection spaces.

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Section: Collision Avoidance In Virtual Environmentsmentioning

confidence: 99%

Section: Collision Avoidance In Virtual Environmentsmentioning

confidence: 99%

Section: Collision Avoidance In Virtual Environmentsmentioning

confidence: 99%

See 1 more Smart Citation

Virtual proxemics: Locomotion in the presence of obstacles in large immersive projection environments

Argelaguet

Olivier

Bruder

et al. 2015

2015 IEEE Virtual Reality (VR)

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“…Certainly, some interactions that pedestrians engage in are physical, but most are not. For example, physics approaches to treating streetscape dynamics may invoke considerations of distance and personal space in collision, which are relevant in examining Newtonian-type forces between pedestrians [88,91], whether through tangible collisions, or as human-perceived repulsion and attraction effects [73,[92][93][94][95][96]. However, decades of socio-behavioral science provide alternative cognitive and social explanations for much of the dynamics presented in physics-based models [45].…”

Section: Physicsmentioning

confidence: 99%

Computational Streetscapes

Torrens

2016

Computation

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Abstract:Streetscapes have presented a long-standing interest in many fields. Recently, there has been a resurgence of attention on streetscape issues, catalyzed in large part by computing. Because of computing, there is more understanding, vistas, data, and analysis of and on streetscape phenomena than ever before. This diversity of lenses trained on streetscapes permits us to address long-standing questions, such as how people use information while mobile, how interactions with people and things occur on streets, how we might safeguard crowds, how we can design services to assist pedestrians, and how we could better support special populations as they traverse cities. Amid each of these avenues of inquiry, computing is facilitating new ways of posing these questions, particularly by expanding the scope of what-if exploration that is possible. With assistance from computing, consideration of streetscapes now reaches across scales, from the neurological interactions that form among place cells in the brain up to informatics that afford real-time views of activity over whole urban spaces. For some streetscape phenomena, computing allows us to build realistic but synthetic facsimiles in computation, which can function as artificial laboratories for testing ideas. In this paper, I review the domain science for studying streetscapes from vantages in physics, urban studies, animation and the visual arts, psychology, biology, and behavioral geography. I also review the computational developments shaping streetscape science, with particular emphasis on modeling and simulation as informed by data acquisition and generation, data models, path-planning heuristics, artificial intelligence for navigation and way-finding, timing, synthetic vision, steering routines, kinematics, and geometrical treatment of collision detection and avoidance. I also discuss the implications that the advances in computing streetscapes might have on emerging developments in cyber-physical systems and new developments in urban computing and mobile computing.

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“…for the social space of an individual p, whose parameters are controlled through certainty, robotic perception capacity and the way in which these influence common social spacing models. We begin by identifying the social spacing models that are prominent within the literature and adopted in the majority of human-robot interaction studies, as follows; (a) concentric circles [1], (b) egg-shape [18], (c) ellipse [23] and (d) dominant-side [24], as sketched at the top of Fig. 2.…”

Section: A Atomic Social Space Functionmentioning

confidence: 99%

Adaptive spacing in human-robot interactions

Papadakis

Rives

Spalanzani

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Social spacing in human-robot interactions is among the main features that is useful when integrating human social intelligence into robotic perception and action skills. One of the main challenges, is to capture the transitions incurred by the human and further take into account robot constraints. Towards this goal, we introduce a novel methodology that can instantiate diverse social spacing models depending on the context and further as a function of uncertainty and robot perception capacity. Our method is based on the use of non-stationary, skew-normal probability density functions for the space of individuals and on treating multi-person space interactions through social mapping. We show the utility of our approach in practice using an indoor robot operating in the presence of humans, allowing it to exhibit socially intelligent responses.

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Characteristics of personal space during obstacle circumvention in physical and virtual environments

Cited by 124 publications

References 21 publications

Virtual proxemics: Locomotion in the presence of obstacles in large immersive projection environments

Virtual proxemics: Locomotion in the presence of obstacles in large immersive projection environments

Computational Streetscapes

Adaptive spacing in human-robot interactions

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