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https://github.com/ayman/hubs-research-acm-chi-2021

Supplemental code and dataset for the ACM CHI 2021 paper on "Proxemics and Social Interactions in an Instrumented Virtual Reality Workshop".
https://github.com/ayman/hubs-research-acm-chi-2021

academic dataset hubs mozilla social social-network-analysis virtualreality vr

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Supplemental code and dataset for the ACM CHI 2021 paper on "Proxemics and Social Interactions in an Instrumented Virtual Reality Workshop".

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README 

        
# hubs-research-acm-chi-2021

Supplimental code and dataset for the [ACM CHI 2021 paper on

"Proxemics and Social Interactions in an Instrumented Virtual Reality

Workshop"](https://doi.org/10.1145/3411764.3445729).  In this research

paper we [instrumented Mozilla Hubs

Cloud](https://github.com/ayman/hubs/tree/hubs-cloud) to record where

participants where during the event.  From there, we measured proxemic

and plotted the activity along with some semi-structured interviews.



[![Animation of avatars moving in main space](3.Notebooks/assets/main_animation_small.gif)](3.Notebooks/assets/main_animation.mp4)



## Preprint



[![Thumbnail of the first page of the

article](3.Notebooks/assets/paperthumb.png)](https://arxiv.org/abs/2101.05300

"PDF on arXiv") 



The [preprint PDF

paper](https://arxiv.org/abs/2101.05300 "PDF on arXiv") is available

on arXiv. 



## Repository Structure

1. For the Mozilla Hubs logger, the [full instrumented Hubs Cloud

   client](https://github.com/ayman/hubs/blob/hubs-cloud/src/systems/research/README.md)

   is in a live fork.  For the purpose of documenation, a snapshot

   (retrieved January 2021) of the A-Frame logger for Hubs is saved

   under the `1.HubsLogger` directory. There is a [longer writeup on

   the

   logger](https://ayman.medium.com/vr-research-in-mozilla-hubs-63fd3002eedf)

   on Medium (or [PDF](1.HubsLogger/VRResearchMozillaHubs.pdf)).

2. The scrubbed data collected from the workshop is in the `poses.csv`

   file in the `2.Data` directory.

3. Python/Jupyter notebooks for resampling, calculating euclidian

   distance, and visualization (of the figures in the paper) are in

   the `3.Notebooks` directory.  To run it, a simple anaconda install

   running `jupyter notebook` will suffice with a few dependencies

   (see the `import` statements in each of the notebooks).

   

## Citing this Notebook, Code, Data, or Repo

Cite the paper, the bespoke logging client, the dataset, or this

notebook as:



Julie Williamson, Jie Li, Vinoba Vinayagamoorthy, David A. Shamma,

Pablo Cesar. 2021.  Proxemics and Social Interactions in an Instrumented 

Virtual Reality Workshop. In _Proceedings of the 2021 CHI Conference on 

Human Factors in Computing Systems_ (CHI '21). Association for Computing 

Machinery, New York, NY, USA, DOI:

https://doi.org/10.1145/3411764.3445729



or 



```

@inproceedings{10.1145/3411764.3445729,

  author =       {Williamson, Julie and Li, Jie and Vinayagamoorthy,

                  Vinoba and Shamma, David A. and Cesar, Pablo},

  title =        {Proxemics and Social Interactions in an Instrumented 

                  Virtual Reality Workshop},

  year =         2021,

  isbn =         978145038096,

  publisher =    {Association for Computing Machinery},

  address =      {New York, NY, USA},

  url =          {https://doi.org/10.1145/3411764.3445729},

  doi =          {10.1145/3411764.3445729},

  abstract =     {Virtual environments (VEs) can create collaborative

                  and social spaces, which are increasingly important

                  in the face of remote work and travel

                  reduction. Recent advances, such as more open and

                  widely available platforms, create new possibilities

                  to observe and analyse interaction in VEs.Using a

                  custom instrumented build of Mozilla Hubs to measure

                  position and orientation, we conducted an academic

                  workshop with a range of typical workshop

                  activities. We analysed social interactions during a

                  keynote, small group breakouts, and informal

                  networking/hallway conversations. Our mixed-method

                  approach combined environment logging, observations,

                  and semi-structured interviews.The results

                  demonstrate how small and large spaces influenced

                  group formation, shared attention, and personal

                  space, where smaller rooms facilitated more cohesive

                  groups while larger rooms made small group formation

                  challenging but personal space more flexible. Beyond

                  our findings, we show how the combination of data

                  and insights can fuel collaborative spaces' design

                  and deliver more effective virtual workshops.},

  booktitle =    {Proceedings of the 2021 CHI Conference on Human

                  Factors in Computing Systems},

  numpages =     13,

  keywords =     {Collaboration, Virtual Reality, Augmented Reality,

                  Dataset, VR, AR},

  location =     {Yokohama, Japan},

  series =       {CHI '21}

}

```



Alternatively, see the [DOI in the ACM Digital

Library](https://doi.org/10.1145/3411764.3445729).



## Licenses

 * All the *code* in this repo is released under [Mozilla Public

License 2.0](https://github.com/ayman/hubs-research-2021/blob/main/LICENSE).

 * The *data* is released under [CC-BY-NC-SA

4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).



## Questions?

You can find our emails on the [ACM DL DOI

page](https://doi.org/10.1145/3411764.3445729) or drop an issue here.