Eye Movement Analysis for Activity Recognition Using Electrooculography

By Andreas Bulling, Jamie A. Ward, Hans-Werner Gellersen, Gerhard Tröster. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009

767 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/3c7589790be1ad34cf9f368083704a39f0723784

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Citations in the corpus

A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, methodology): “Feature Extraction from Forehead EOG After preprocessing forehead EOG signals and extracting VEO f and HEO f , we detected eye movements such as blinks and saccades using the wavelet transform method (Bulling et al. 2011).”
What do eyes reveal about the mind?: Algorithmic inference of search targets from fixations (2015, background): “There is a rich collection of literature that discusses the role of oculomotor behavior in tasks as diverse as reading [69,22], pattern copying [1], portrait painting [55], visual search [80,84,88], tea making [44], sandwich making [32], fencing [29], cricket [46], squash [21], billiards [23], juggl”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, unknown): “[202] proposed a method for human”
Recognition of visual memory recall processes using eye movement analysis (2011, methodology): “We showed that the movement patterns the eyes perform during different activities carry information that allows to recognise the activities themselves [6].”
A tutorial on human activity recognition using body-worn inertial sensors (2014, methodology): “Physiological signals, such as electro-oculography (EOG), typically require preprocessing algorithms for denoising or baseline drift removal [Bulling et al. 2011].”
It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation (2016, methodology): “ works in computer vision have studied the problem of estimating human eye gaze [7] given its importance for a range of applications, such as gaze-based human-robot interaction [21] or user modelling [3]. While early methods typically required settings in which lighting conditions or head pose could”
Seeing with Humans: Gaze-Assisted Neural Image Captioning (2016, methodology): “Consequently, gaze has successfully been used for tasks such as eye-based user modeling [1], [2], [3], [4], [5] and opened up new opportunities to further advance human-machine collaboration – collaborative human-machine vision systems in which part of the processing is carried out by the machine…”
Driver-Activity Recognition in the Context of Conditionally Autonomous Driving (2015, methodology): “3 with the appropriate notation as introduced by [12].”
Combining Planning with Gaze for Online Human Intention Recognition (2018, background): “reading, browsing watching videos) through the implicit monitoring of eye movements ([5, 16]).”
A Comprehensive Survey of Deep Learning for Image Captioning (2018, background): “Thus, gaze mechanisms have already shown their potential performances in eye-based user modeling [18, 35, 109, 122, 124], object localization [100] or recognition [67] and holistic scene understanding [158, 160].”
Rendering of Eyes for Eye-Shape Registration and Gaze Estimation (2015, background): “Therefore they are important for a range of applications including gaze-based human-computer interaction [2], visual behavior monitoring [3], and – more recently – collaborative human-computer vision systems [4, 5].”
Human Emotion Recognition: Review of Sensors and Methods (2020, methodology): “Arousal level EOG Commercial system Mobi from Twente Medical Systems International (TMSI) [168]”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “The signal can be used to detect eye movements [89].”
Privacy-aware eye tracking using differential privacy (2018, methodology): “y. Eye Movement Feature Extraction. We extracted a total of 52 eye movement features, covering fixations, saccades, blinks, and pupil diameter (see Table 1 in the supplementary material). Similar to [Bulling et al. 2011b], we also computed wordbook features that encode sequences of n saccades. We ex”
Eye Tracking and Eye-Based Human–Computer Interaction (2014, background): “…for the ﬁrst time that a variety of visual and non-visual human activities, such as reading (Bulling et al. 2008a, 2012b) or common ofﬁce activities (Bulling et al. 2009c, 2011b), could be spotted and recognized automatically by analyzing features based solely on eye movement patterns—independent…”