Visual Multi-Metric Grouping of Eye-Tracking Data
We present an algorithmic and visual grouping of participants and eye-tracking metrics derived from recorded eye-tracking data. Our method utilizes two well-established visualization concepts. First, parallel coordinates are used to provide an overview of the used metrics, their interactions, and si...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-02-01
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| Series: | Journal of Eye Movement Research |
| Subjects: | |
| Online Access: | https://bop.unibe.ch/JEMR/article/view/4225 |
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| _version_ | 1850178373111775232 |
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| author | Ayush Kumar Rudolf Netzel Michael Burch Daniel Weiskopf Klaus Mueller |
| author_facet | Ayush Kumar Rudolf Netzel Michael Burch Daniel Weiskopf Klaus Mueller |
| author_sort | Ayush Kumar |
| collection | DOAJ |
| description | We present an algorithmic and visual grouping of participants and eye-tracking metrics derived from recorded eye-tracking data. Our method utilizes two well-established visualization concepts. First, parallel coordinates are used to provide an overview of the used metrics, their interactions, and similarities, which helps select suitable metrics that describe characteristics of the eye-tracking data. Furthermore, parallel coordinates plots enable an analyst to test the effects of creating a combination of a subset of metrics resulting in a newly derived eye-tracking metric. Second, a similarity matrix visualization is used to visually represent the affine combination of metrics utilizing an algorithmic grouping of subjects that leads to distinct visual groups of similar behavior. To keep the diagrams of the matrix visualization simple and understandable, we visually encode our eye- tracking data into the cells of a similarity matrix of participants. The algorithmic grouping is performed with a clustering based on the affine combination of metrics, which is also the basis for the similarity value computation of the similarity matrix. To illustrate the usefulness of our visualization, we applied it to an eye-tracking data set involving the reading behavior of metro maps of up to 40 participants. Finally, we discuss limitations and scalability issues of the approach focusing on visual and perceptual issues. |
| format | Article |
| id | doaj-art-c49fde2f596b4cd7ac8ec4820002867b |
| institution | OA Journals |
| issn | 1995-8692 |
| language | English |
| publishDate | 2018-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Eye Movement Research |
| spelling | doaj-art-c49fde2f596b4cd7ac8ec4820002867b2025-08-20T02:18:46ZengMDPI AGJournal of Eye Movement Research1995-86922018-02-0110510.16910/jemr.10.5.10Visual Multi-Metric Grouping of Eye-Tracking DataAyush Kumar0Rudolf Netzel1Michael Burch2Daniel Weiskopf3Klaus Mueller4Stony Brook University, New YorkUniversity of StuttgartEindhoven Uni. of Tech., NetherlandsUniversity of StuttgartStony Brook University, New YorkWe present an algorithmic and visual grouping of participants and eye-tracking metrics derived from recorded eye-tracking data. Our method utilizes two well-established visualization concepts. First, parallel coordinates are used to provide an overview of the used metrics, their interactions, and similarities, which helps select suitable metrics that describe characteristics of the eye-tracking data. Furthermore, parallel coordinates plots enable an analyst to test the effects of creating a combination of a subset of metrics resulting in a newly derived eye-tracking metric. Second, a similarity matrix visualization is used to visually represent the affine combination of metrics utilizing an algorithmic grouping of subjects that leads to distinct visual groups of similar behavior. To keep the diagrams of the matrix visualization simple and understandable, we visually encode our eye- tracking data into the cells of a similarity matrix of participants. The algorithmic grouping is performed with a clustering based on the affine combination of metrics, which is also the basis for the similarity value computation of the similarity matrix. To illustrate the usefulness of our visualization, we applied it to an eye-tracking data set involving the reading behavior of metro maps of up to 40 participants. Finally, we discuss limitations and scalability issues of the approach focusing on visual and perceptual issues.https://bop.unibe.ch/JEMR/article/view/4225Eye movementmetricseye trackingvisualizationparallel coordinatessaccades |
| spellingShingle | Ayush Kumar Rudolf Netzel Michael Burch Daniel Weiskopf Klaus Mueller Visual Multi-Metric Grouping of Eye-Tracking Data Journal of Eye Movement Research Eye movement metrics eye tracking visualization parallel coordinates saccades |
| title | Visual Multi-Metric Grouping of Eye-Tracking Data |
| title_full | Visual Multi-Metric Grouping of Eye-Tracking Data |
| title_fullStr | Visual Multi-Metric Grouping of Eye-Tracking Data |
| title_full_unstemmed | Visual Multi-Metric Grouping of Eye-Tracking Data |
| title_short | Visual Multi-Metric Grouping of Eye-Tracking Data |
| title_sort | visual multi metric grouping of eye tracking data |
| topic | Eye movement metrics eye tracking visualization parallel coordinates saccades |
| url | https://bop.unibe.ch/JEMR/article/view/4225 |
| work_keys_str_mv | AT ayushkumar visualmultimetricgroupingofeyetrackingdata AT rudolfnetzel visualmultimetricgroupingofeyetrackingdata AT michaelburch visualmultimetricgroupingofeyetrackingdata AT danielweiskopf visualmultimetricgroupingofeyetrackingdata AT klausmueller visualmultimetricgroupingofeyetrackingdata |