| Project/Area Number |
17K00276
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Human interface and interaction
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
Dengel Andreas 大阪府立大学, 研究推進機構, 客員教授 (00773574)
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| Co-Investigator(Kenkyū-buntansha) |
石丸 翔也 大阪府立大学, 研究推進機構, 客員研究員 (10788730)
黄瀬 浩一 大阪府立大学, 工学(系)研究科(研究院), 教授 (80224939)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | アイトラッキング / 可視性 / 可読性 / 皮膚電気活動 / 読書行動解析 / 文書解析 / データ拡張 / 人工知能 / eye tracking / readability / legibility / visualization / fixation / electrodermal activity / 認知科学 |
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| Outline of Final Research Achievements |
In this research, we aim to evaluate the readability of a document on the basis of eye movements. Since it is not realistic to ask participants to read documents with eye tracking devices every time, we develop a system that synthesizes artificial eye movements on the document and utilizes them for the readability measurement. In the first year, as basic research for the readability measurement, we investigated the relationship between cognitive load while reading and sensor signals including eye movements and electrodermal activities. In the next year, We proposed a method to estimate fixation durations of each word of a document without asking people to read the document with an eye tracker. In the final year, we improved the performance of gaze synthesis by conducting a well-controlled experiment and applied it to the readability measurement.
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| Academic Significance and Societal Importance of the Research Achievements |
学術的観点では，生成された眼球運動は深層学習のための人工的なデータセットとして利用することができる．深層学習においてはデータ量の多寡が問題となるため，データセットの拡張は活動認識の研究を加速させるものである．社会的インパクトとしては，出版，広告，教育への貢献が期待される．教科書は最も重要な知識源である．本プロジェクトの成果により，適切なレイアウトやコンテンツを提供することで学生の認知負荷を軽減することができる．
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