NMF-Based Analysis of Mobile Eye-Tracking Data

Authors. Daniel Klötzl, Tim Krake, Frank Heyen, Michael Becher, Maurice Koch, Daniel Weiskopf, Kuno Kurzhals

Venue. ETRA (2024) Workshop Paper

Type. Workshop Paper

Materials. DOI ACM PDF

Abstract. The depiction of scanpaths from mobile eye-tracking recordings by thumbnails from the stimulus allows the application of visual computing to detect areas of interest in an unsupervised way. We suggest using nonnegative matrix factorization (NMF) to identify such areas in stimuli. For a user-defined integer k, NMF produces an explainable decomposition into k components, each consisting of a spatial representation associated with a temporal indicator. In the context of multiple eye-tracking recordings, this leads to k spatial representations, where the temporal indicator highlights the appearance within recordings. The choice of k provides an opportunity to control the refinement of the decomposition, i.e., the number of areas to detect. We combine our NMF-based approach with visualization techniques to enable an exploratory analysis of multiple recordings. Finally, we demonstrate the usefulness of our approach with mobile eye-tracking data of an art gallery.

@inproceedings{10.1145/3649902.3653518,
    title        = {NMF-Based Analysis of Mobile Eye-Tracking Data},
    author       = {Kl\"{o}tzl, Daniel and Krake, Tim and Heyen, Frank and Becher, Michael and Koch, Maurice and Weiskopf, Daniel and Kurzhals, Kuno},
    year         = {2024},
    booktitle    = {Proceedings of the 2024 Symposium on Eye Tracking Research and Applications},
    publisher    = {ACM},
    series       = {ETRA '24},
    doi          = {10.1145/3649902.3653518},
    url          = {https://doi.org/10.1145/3649902.3653518},
    abstract     = {The depiction of scanpaths from mobile eye-tracking recordings by thumbnails from the stimulus allows the application of visual computing to detect areas of interest in an unsupervised way. We suggest using nonnegative matrix factorization (NMF) to identify such areas in stimuli. For a user-defined integer k, NMF produces an explainable decomposition into k components, each consisting of a spatial representation associated with a temporal indicator. In the context of multiple eye-tracking recordings, this leads to k spatial representations, where the temporal indicator highlights the appearance within recordings. The choice of k provides an opportunity to control the refinement of the decomposition, i.e., the number of areas to detect. We combine our NMF-based approach with visualization techniques to enable an exploratory analysis of multiple recordings. Finally, we demonstrate the usefulness of our approach with mobile eye-tracking data of an art gallery.},
    articleno    = {76},
    numpages     = {9},
    keywords     = {Clustering, Eye Tracking, Matrix Factorization, NMF, Visualization}
}