Immersive walking in a virtual crowd: The effects of the density, speed, and direction of a virtual crowd on human movement behavior

Koilias, Alexandros; Nelson, Michael G.; Anagnostopoulos, Christos‐Nikolaos; Mousas, Christos

doi:10.1002/cav.1928

Cited by 23 publications

(24 citation statements)

References 60 publications

(123 reference statements)

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“…Crowd simulations can be designed using either a macroscopic approach, which views a crowd as a single unit with properties related to fluids, or a microscopic approach, which involves modeling the behaviors of individual agents and their local interactions 3,7 . In designing virtual crowds, an essential requirement is to create human‐like steering behaviors to maintain realism, which can be achieved through either algorithmic simulation or animation 8 . In general, crowd movements emerge from a combination of local interactions between neighboring agents in a crowd 9 .…”

Section: Related Workmentioning

confidence: 99%

Human‐virtual crowd interaction: Towards understanding the effects of crowd avoidance proximity in an immersive virtual environment

Trivedi,

Mousas

2023

Computer Animation & Virtual

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Summary This paper focuses on understanding how study participants interact and perceive a virtual crowd in an immersive virtual environment. Specifically, our within‐group exploratory study investigated how avoidance proximity variations (i.e., low, medium, and high avoidance proximity [defined as avoidance radius]) assigned to crowd agents impacted participants' interaction with the virtual crowd. During the study, we instructed our participants to walk in a virtual environment. At the same time, we had a virtual crowd scripted to walk toward the start position of the participant following a straight path. During the participants' walking task, we collected movement data (i.e., trajectory length and completion time) and immediately after each experimental condition, we asked participants to self‐report their experience (i.e., co‐presence, behavioral independence, crowd realism, crowd interaction realism, perceived politeness, and emotional reactivity). Based on the collected data, we found that when we exposed our participants to the high avoidance proximity condition, they: (1) followed longer paths, (2) spent more time reaching the target goal, (3) rated the virtual crowd less polite, (4) rated the virtual crowd and their interaction with the virtual crowd less realistic, (5) rated the behavior independence of the virtual crowd lower, and (6) self‐reported higher emotional reactivity. We discuss our findings and suggestions for further research on human‐virtual crowd interaction.

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Section: Related Workmentioning

confidence: 99%

Human‐virtual crowd interaction: Towards understanding the effects of crowd avoidance proximity in an immersive virtual environment

Trivedi,

Mousas

2023

Computer Animation & Virtual

Self Cite

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“…Note, however, that not only proxemics is relevant, but also that the level of crowdedness affects how people feel with respect to their environment [32]. In VR, crowd simulation studies show that people restrain their space when walking in a crowd [46]. Dickinson et al [47] show that crowdedness produces negative feelings and can, thus, disrupt the user experience in Social VR.…”

Section: Social Virtual Realitymentioning

confidence: 99%

Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience

Welsch

Villa²,

Chuang

et al. 2022

BDCC

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Virtual reality is increasingly used for tasks such as work and education. Thus, rendering scenarios that do not interfere with such goals and deplete user experience are becoming progressively more relevant. We present a physiologically adaptive system that optimizes the virtual environment based on physiological arousal, i.e., electrodermal activity. We investigated the usability of the adaptive system in a simulated social virtual reality scenario. Participants completed an n-back task (primary) and a visual detection (secondary) task. Here, we adapted the visual complexity of the secondary task in the form of the number of non-player characters of the secondary task to accomplish the primary task. We show that an adaptive virtual reality can improve users’ comfort by adapting to physiological arousal regarding the task complexity. Our findings suggest that physiologically adaptive virtual reality systems can improve users’ experience in a wide range of scenarios.

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“…Some authors are interested in human walking trajectories, especially during collision or obstacles avoidance (Basili et al 2013;Heijnen et al 2012) while others studied and modelled the human joint trajectories during gait (Nandi et al 2016;Semwal et al 2018). Some more recent studies focus on the trajectories of human walking in crowded environment (Koilias et al 2020;Zhu et al 2020). However, to the best of our knowledge, the problem of the modeling and the simulation of common human trajectories during locomotion is not frequently raised.…”

Section: State Of the Artmentioning

confidence: 99%

Inverse optimal control to model human trajectories during locomotion

Maroger

Stasse

Watier

2021

Computer Methods in Biomechanics and Biomedical Engineering

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Cobotic applications require a good knowledge of human behaviour in order to be cleverly, securely and fluidly performed. For example, to make a human and a humanoid robot perform a co-navigation or a co-manipulation task, a model of human walking trajectories is essential to make the robot follow or even anticipate the human movements. This paper aims to study the Center of Mass (CoM) path during locomotion and generate human-like trajectories with an optimal control scheme. It also proposes a metric which allows to assess this model compared to the human behaviour. CoM trajectories during locomotion of 10 healthy subjects were recorded and analysed as part of this study. Inverse optimal control was used to find the optimal cost function which best fits the model to the measurements. Then, the measurements and the generated data were compared in order to assess the performance of the presented model. Even if the experiments show a great variability in human behaviours, the model presented in this study gives an accurate approximation of the average human walking trajectories. Furthermore, this model gives an approximation of human locomotion good enough to improve cobotic tasks allowing a humanoid robot to anticipate the human behaviour.

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Immersive walking in a virtual crowd: The effects of the density, speed, and direction of a virtual crowd on human movement behavior

Cited by 23 publications

References 60 publications

Human‐virtual crowd interaction: Towards understanding the effects of crowd avoidance proximity in an immersive virtual environment

Human‐virtual crowd interaction: Towards understanding the effects of crowd avoidance proximity in an immersive virtual environment

Virtual Reality Adaptation Using Electrodermal Activity to Support the User Experience

Inverse optimal control to model human trajectories during locomotion

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