| Project/Area Number |
18K12118
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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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| Review Section |
Basic Section 90130:Medical systems-related
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| Research Institution | Suwa University of Science |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
坂本 麻衣子 佐賀大学, 医学部, 准教授 (10720196)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2019: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2018: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
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| Keywords | MCI早期発見 / VR-IADL / 行動特徴解析 / Virtual Reality / Micro-error / 動作プリミティブ / 時系列セグメンテーション / MEの発生確率 / IADL / マイクロエラー |
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| Outline of Final Research Achievements |
In this study, in order to establish the recognition technique of the stagnation of movement during IADL task called Micro-error (ME), the generation condition was investigated. And, the segmentation technique for dividing the IADL action into the unit was studied from the viewpoint of motion minimum unit called motion primitive in order to recognize ME from motion data of the time series. In the generation condition, it was clarified that the difficulty in IADL task affected the generation probability of ME. And, in the research on the segmentation technique, the model which realized the segmentation of operation primitive unit including ME at 93.7% accuracy was developed.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の目的を達成したことにより，Micro-errorという行動指標に基づいたMCIのリスクやADへの進行度合いを定量的に可視化するための基盤技術が確立した．これにより，医療従事者が多角的な視点からMCIのリスクを判断でき，計測環境の簡易化も可能となるため，認知症の専門家不足に伴う診断の遅れといった問題の解消に貢献できる．さらに，専門家だけでなく介護者や患者自身にもリスクや進行度合いのフィードバックや共有が可能となる．従って，今後さらなる増加が予想される認知症患者に対するケアや予防において，大幅な人的・物理的医療コストの削減につながる．
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