| Project/Area Number |
21K11876
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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 60060:Information network-related
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| Research Institution | Tokyo University of Technology |
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Principal Investigator |
WU BO 東京工科大学, コンピュータサイエンス学部, 助教 (70802031)
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| Co-Investigator(Kenkyū-buntansha) |
呉 鳶 愛国学園大学, 人間文化学部, 准教授 (30822423)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2023: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2022: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2021: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Keywords | 草刈り動作解析 / 農作業経験の可視化 / 経験的モード分解 / 身体運動計測技術の応用 / 農作業安全対策 / motion analysis / human factors / joints angles analysis / human centric computing / mowing behaviors / fall detection / elderly support / Hilbert Huang transform / motion measurement / human behavior analysis / mowing patterns analysis |
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| Outline of Research at the Start |
We focus on the issues of safe mowing in complex geographic shapes environment. According to the collection of the data of body movement, eye movements and environmental factors from the mowing workers, a standard model of safer mowing will be constructed through big data-based comparison analysis.
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| Outline of Final Research Achievements |
The research team conducted surveys and experiments in terraced paddy fields located in a mountainous area. We collected data on the physical movements of skilled mowing operators, with a focus on individuals predominantly over the age of 65. We utilized a variety of analytical methods to identify factors that contribute to work safety. This analysis was conducted based on the classification of movement patterns among skilled mowing operators. It was found that efficient patterns have a higher risk of falling. As a result of analyzing the mowing motion by breaking it down into sub-actions based on joint angle calculations, we found differences and similarities in the frequency and amplitude of the sub-actions (pre-cutting) among mowing operators with different levels of experience. Additionally, we observed that physical characteristics have effects on the stability of the mowing operators. The above research results were published in academic papers and other scholarly publications.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、凸凹のある急峻な法面における安全な草刈り作業パターンを特定し、熟練草刈り作業者の経験を可視化する方法として、草刈作業時の動作の細分化に関する分析手法を構築した。これらの実用性のある結果は、新人を対象とする教育とトレーニングに有用な知見を提供するとともに、草刈り転倒リスク検知システムを開発するためのデータ蓄積としても活用できる。それに加え、本研究で構築した分析手法は、今後の人間身体動作の分析にも活用されることが期待できる。
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