EyeMSA: Exploring Eye Movement Data with Pairwise and Multiple Sequence Alignment

被引：11

作者：

Burch, Michael ^{[1
]}

Kurzhals, Kuno ^{[2
]}

Kleinhans, Niklas ^{[2
]}

Weiskopf, Daniel ^{[2
]}

机构：

[1] Eindhoven Univ Technol, Eindhoven, Netherlands

[2] Univ Stuttgart, Stuttgart, Germany

来源：

2018 ACM SYMPOSIUM ON EYE TRACKING RESEARCH & APPLICATIONS (ETRA 2018) | 2018年

关键词：

Eye tracking; Scan paths; Multiple sequence alignment; Consensus matrix visualization; Visual analytics;

D O I：

10.1145/3204493.3204565

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Eye movement data can be regarded as a set of scan paths, each corresponding to one of the visual scanning strategies of a certain study participant. Finding common subsequences in those scan paths is a challenging task since they are typically not equally temporally long, do not consist of the same number of fixations, or do not lead along similar stimulus regions. In this paper we describe a technique based on pairwise and multiple sequence alignment to support a data analyst to see the most important patterns in the data. To reach this goal the scan paths are first transformed into a sequence of characters based on metrics as well as spatial and temporal aggregations. The result of the algorithmic data transformation is used as input for an interactive consensus matrix visualization. We illustrate the usefulness of the concepts by applying it to formerly recorded eye movement data investigating route finding tasks in public transport maps.

引用

页数：5

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