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Updated: Jun 20, 2025

Movement Retraining using Real-time Feedback of Performance

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Published on: January 17, 2013

机器学习的运动模式的变化,在基尼克特基于混合现实炼程序在女性可能的萨科佩尼亚:试点研究的试点研究.

Yunho Sung¹, Ji-Won Seo¹, Byunggul Lim^1,2

¹Department of Physical Education, Seoul National University, Seoul, Korea.

Annals of geriatric medicine and research

|July 18, 2024

概括

运动干预措施改善了老年妇女的运动模式,可能有肉症,降低了跌倒风险. 本研究提供了远程运动监控系统的数据.

关键词:

增强现实是一种增强现实.机器学习是机器学习.运动运动运动阻力训练阻力训练阻力训练萨尔科佩尼亚是什么意思萨尔科佩尼亚

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科学领域:

老年学是指老年学的学科.
生物力学生物力学
运动科学运动科学

背景情况:

肉症是老年人肌肉消耗的疾病,改变了运动模式,增加了跌倒和受伤的风险.
了解这些运动变化对于开发有效的干预和监测策略至关重要.

研究的目的:

调查运动干预措施对可能患有肉症的老年妇女运动模式的影响.
为了比较运动运动动力学在运动期间有和没有sarcopenia的个体之间.
探索远程监测老年人运动的潜力.

主要方法:

招募了老年女性 (≥65岁) 进入可能的肉类症组 (PSG) 和基于膝盖伸展强度 (KES) 的参考组 (RG).
为PSG实施了为期8周的运动干预,为RG实施了为期3周的运动干预.
记录和分析运动期间的动力运动数据,重点关注在宽 squats 期间的膝盖旋转.

主要成果:

与RG相比,PSG在宽 squats期间表现出更大的膝盖旋转,与下肢疲软有关.
一个投票分类器证实了群体之间的持续移动模式差异,直到干预结束.
干预后,PSG显示膝盖旋转减少和运动稳定,减少群体差异.

结论:

运动干预措施有效地改变了老年人可能患有肉症的运动模式.
改善下肢强度导致更安全,更稳定的运动.
研究结果支持为老年人开发远程运动管理系统.