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". In this research paper we instrumented Mozilla 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.

Preprint

The preprint PDF paper is available on arXiv.

Repository Structure

1. For the Mozilla Hubs logger, the full instrumented Hubs Cloud client 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 on Medium (or 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.

Licenses

• All the code in this repo is released under Mozilla Public License 2.0.
• The data is released under CC-BY-NC-SA 4.0.

Questions?

You can find our emails on the ACM DL DOI page or drop an issue here.