LAUSR

The observer effect found in laboratory studies has long posed a problem for researchers. In-the-wild studies reduce the observer effect, but have problems with gathering accurately labeled data usable for training algorithms. Manual labeling is time-consuming, obtrusive, and unfeasible, and if done by the researchers, it potentially violates the privacy of the participants. In this article, we present a labeling workflow based on an in-the-wild study that investigated cognitive state changes through eye-gaze in naturalistic settings. We contribute a setup that enables participants to label their data unobtrusively and quickly. We use J!NS MEME electrooculography glasses, Narrative Clip 2 wearable cameras, and a proprietary data tagging software package. Our setup is reproducible for field studies, preserves data integrity, and maintains participant privacy. This workflow can be extended to other studies in pervasive and ubiquitous computing and is especially suitable for deployment in the pandemic and postpandemic world.