The Everyday Mobile Visual Attention Dataset

Mihai Bâce Sander Staal Andreas Bulling
ETH Zürich ETH Zürich University of Stuttgart
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Abstract

We present the first real-world dataset and quantitative evaluation of visual attention of mobile device users in-situ, i.e. while using their devices during everyday routine. Understanding user attention is a core research challenge in mobile HCI but previous approaches relied on usage logs or self-reports that are only proxies and consequently do neither reflect attention completely nor accurately. Our evaluations are based on Everyday Mobile Visual Attention (EMVA) – a new 32-participant dataset containing around 472 hours of video snippets recorded over more than two weeks in real life using the front-facing camera as well as associated usage logs, interaction events, and sensor data. Using an eye contact detection method, we are first to quantify the highly dynamic nature of everyday visual attention across users, mobile applications, and usage contexts. We discuss key insights from our analyses that highlight the potential and inform the design of future mobile attentive user interfaces.

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Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

To download the dataset, please contact Mihai Bâce (mihai.bace@inf.ethz.ch). We are currently in the process of setting up a permanent host for our dataset.

The data is only to be used for non-commercial scientific purposes. If you use this dataset in any of your work, please cite the following paper:

  • Quantification of Users' Visual Attention During Everyday Mobile Device Interactions. M. Bâce, S. Staal and A. Bulling. In Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), 2020.
@inproceedings{bace20_chi,
  title = {Quantification of Users' Visual Attention During Everyday Mobile Device Interactions},
  author = {Bâce, Mihai and Staal, Sander and Bulling, Andreas},
  year = {2020},
  booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)},
  doi = {10.1145/3313831.3376449}
}

EMVA Dataset

A detailed description of the dataset will be available soon.

Contact

If you have any questions, feel free to reach out: Mihai Bâce (mihai.bace@inf.ethz.ch)