Investigation of the Effect of Physical Ability on the Fall Mitigation Motion Using the Combination of Experiment and Simulation
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- Yasuhiro Akiyama
- Faculty of Textile Science and Technology, Shinshu University, Nagano 386-8567, Japan
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- Shuto Yamada
- Department of Mechanical Systems Engineering, Nagoya University, Aichi 464-8601, Japan
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- Shogo Okamoto
- Department of Computer Science, Tokyo Metropolitan University, Tokyo 191-0065, Japan
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- Yoji Yamada
- National Institute of Technology, Toyota College, Aichi 471-0067, Japan
書誌事項
- 公開日
- 2024-04-04
- 資源種別
- journal article
- 権利情報
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- https://creativecommons.org/licenses/by/4.0/
- DOI
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- 10.3390/app14073051
- 公開者
- MDPI AG
説明
<jats:p>The simulation of fall plays a critical role in estimating injuries caused by fall. However, implementing human fall mitigation motions on a simulator proves challenging due to the complexity and variability of fall movement. Our simulator estimates fall motion by extrapolating the motion observed in fall experiments. By incorporating actual fall motion data for the upper limbs, we enhanced the realism of the fall simulation. The application of forward dynamics control to the lower limbs allowed for the adjustment of mitigation motions, taking into account individual physical capabilities. In this study, fall simulations were conducted under the constraints of maximum joint torque and maximum torque change rate, emulating the physical capabilities of both the elderly and young adults. Our results successfully demonstrated the mitigation motion facilitated by the stance leg reduced the descent velocity of the center of mass by 0.75 m/s for elderly individuals and by 1.25 m/s for young adults, compared to a zero torque condition. This indicates that our study introduced a novel method for quantifying the impact of the lower limbs’ physical capabilities on fall velocity. Such a method represents a significant advancement in understanding how mitigation motions can influence fall injury simulations.</jats:p>
収録刊行物
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- Applied Sciences
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Applied Sciences 14 (7), 3051-, 2024-04-04
MDPI AG
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キーワード
詳細情報 詳細情報について
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- CRID
- 1360021391887151872
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- ISSN
- 20763417
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- 資料種別
- journal article
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- データソース種別
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- Crossref
- KAKEN
- OpenAIRE
