Sabrina Hoppe (PhD Student)

MSc Sabrina Hoppe

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - Room 622
Phone
+49 681 9325 2000
Fax
+49 681 9325 2099
Email
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Personal Information

Education

I hold a Bachelor’s degree in Computer Science from Saarland University (see the project my Bachelor’s dissertation became part of here). During my undergrad I was additionally enrolled for psychology and mainly focused on cognitive psychology. Instead of a second Bachelor’s degree however, I pursued an interdisciplinary Master’s degree in Cognitive Science at Edinburgh University in 2013/14 that I was awarded a distinction for (see my final project here).

Since April 2015 my PhD has been supported by a scholarship of the German National Academic Foundation.

Research Interests

  • Machine Learning and Pattern Recognition
  • Eye Tracking
  • Cognitive Modeling
  • Human-Computer Interaction

Publications

2016
End-to-End Eye Movement Detection Using Convolutional Neural Networks
S. Hoppe and A. Bulling
Technical Report, 2016
(arXiv: 1609.02452)
Abstract
Common computational methods for automated eye movement detection - i.e. the task of detecting different types of eye movement in a continuous stream of gaze data - are limited in that they either involve thresholding on hand-crafted signal features, require individual detectors each only detecting a single movement, or require pre-segmented data. We propose a novel approach for eye movement detection that only involves learning a single detector end-to-end, i.e. directly from the continuous gaze data stream and simultaneously for different eye movements without any manual feature crafting or segmentation. Our method is based on convolutional neural networks (CNN) that recently demonstrated superior performance in a variety of tasks in computer vision, signal processing, and machine learning. We further introduce a novel multi-participant dataset that contains scripted and free-viewing sequences of ground-truth annotated saccades, fixations, and smooth pursuits. We show that our CNN-based method outperforms state-of-the-art baselines by a large margin on this challenging dataset, thereby underlining the significant potential of this approach for holistic, robust, and accurate eye movement protocol analysis.
2015
On the Interplay between Spontaneous Spoken Instructions and Human Visual Behaviour in an Indoor Guidance Task
N. Koleva, S. Hoppe, M. M. Moniri, M. Staudte and A. Bulling
37th Annual Meeting of the Cognitive Science Society (COGSCI 2015), 2015
Walking Reduces Spatial Neglect
T. Loetscher, C. Chen, S. Hoppe, A. Bulling, S. Wignall, C. Owen, N. Thomas and A. Lee
Journal of the International Neuropsychological Society, 2015
Recognition of Curiosity Using Eye Movement Analysis
S. Hoppe, T. Loetscher, S. Morey and A. Bulling
UbiComp & ISWC’15, ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015