Measuring audience attention towards pervasive displays is important but accurate measurement in real time remains a significant sensing challenge. Consequently, researchers and practitioners typically use other features, such as face presence, as a proxy. We provide a principled comparison of the performance of six features and their combinations for measuring attention: face presence, movement trajectory, walking speed, shoulder orientation, head pose, and gaze direction. We implemented a prototype that is capable of capturing this rich set of features from video and depth camera data. Using a controlled lab experiment (N=18) we show that as a single feature, face presence is indeed among the most accurate. We further show that accuracy can be increased through a combination of features (+10.3%), knowledge about the audience (+63.8%), as well as user identities (+69.0%). Our findings are valuable for display providers who want to collect data on display effectiveness or build interactive, responsive apps.

@inproceedings{alt16_dis,
  title = {Attention, please! Comparing Features for Measuring Audience Attention Towards Pervasive Displays},
  author = {Florian Alt and Andreas Bulling and Lukas Mecke and Daniel Buschek},
  year = {2016},
  booktitle = {Proc. ACM SIGCHI Conference on Designing Interactive Systems (DIS)},
  pages = {823-828},
  doi = {10.1145/2901790.2901897},
}