| Project/Area Number |
16K12838
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Konan University |
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Principal Investigator |
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | ヒューマンインタフェース / 機器・人間の信頼性 / ウェアラブルデバイス / 人間信頼性工学 |
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| Outline of Final Research Achievements |
The aim of this study is to propose a method of evaluating for level of driver’s safety margin by using machine learning methods. In order to examine parameters related to visual behavior effective for evaluating driver's safety margin in driving, EOG and head movements were measured during simulated driving. In the experiment, In the experiment, the distinguish the "low safety margin" condition from "high safety margin" condition was attempted by using machine learning methods of Adaboost on data related to EOG and head movements. Through cross-validation using the data from one participant as test data and data from the others as training data, Adaboost method was determined to have the correct discrimination rate (over 80%). These results suggest the possibility of evaluating safety margin by using wearable device.
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| Academic Significance and Societal Importance of the Research Achievements |
眼電図計測は，眼球の角膜側に正，網膜側に負の電位が帯電している特性を利用し，随意性の瞬目判定や眼球回転による応答を利用したコミュニケーション支援に関する研究に多く用いられてきたが，注視点や視点移動量の評価には計測精度の問題でほとんど使われていない．そのため，本研究で眼電図から注視点や視点移動量の評価法についての有効性を検討することは，学術的価値も非常に高い．さらに，眼電図が簡単に計測できる眼鏡型ウェアラブルデバイスの開発が進められており，本研究で提案する手法が確立できれば装着の違和感がないドライバーの状態把握システムとして実用化の可能性が極めて高く，非常に意義のある研究課題である．
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