The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Eye Movement Analysis for Activity Recognition Using Electrooculography
Open accessAbstract
In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals-saccades, fixations, and blinks-and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.
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Duchowski, A. T. (2003) · Springer eBooks · doi:10.1007/978-1-4471-3750-4 · Cited by 2,242
Identifying fixations and saccades in eye-tracking protocols
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Machine Recognition of Human Activities: A Survey
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Eye movements in natural behavior
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Eye Tracking Methodology; Theory and Practice
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Self-citationEmotionMeter: A Multimodal Framework for Recognizing Human Emotions
Zheng, W.-L., Liu, W., Lu, Y., Lu, B.-L., & Cichocki, A. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2797176 · Cited by 1,276
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879
Eye Tracking and Eye-Based Human–Computer Interaction
Majaranta, P., & Bulling, A. (2014) · Human–Computer Interaction Series · doi:10.1007/978-1-4471-6392-3_3 · Cited by 530
Self-citationA multimodal approach to estimating vigilance using EEG and forehead EOG
Zheng, W.-L., & Lu, B.-L. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa5a98 · Cited by 424
Metadata
- Authors
- Bulling, A., Ward, J. A., Gellersen, H., & Tröster, G.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2011-04
- Volume
- 33
- Issue
- 4
- Pages
- 741-753
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2126698740
- Cited by
- 718
- References
- 49
- FWCI
- 41.30
- Type
- journal article
- Language
- en
- Open access
- green
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- Bulling, A., Ward, J. A., Gellersen, H., & Tröster, G. (2011). Eye Movement Analysis for Activity Recognition Using Electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2010.86
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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