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相关实验视频

Updated: May 21, 2025

Design and Analysis for Fall Detection System Simplification
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使用加权KNN机器学习算法预测老年人辅助设备的性能.

S Vaisali1, C Maheswari1, S Shankar2,3

  • 1Department of Mechatronics Engineering, Kongu Engineering College, Erode, Tamil Nadu, India.

Journal of back and musculoskeletal rehabilitation
|March 19, 2025
PubMed
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这项研究表明,上肢外骨可以在举重运动期间减少老年人肌肉疲劳. 一个机器学习算法预测设备适合个人,帮助辅助技术开发.

科学领域:

  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术
  • 医疗保健中的人工智能

背景情况:

  • 老龄化往往导致老年人肌肉力量和耐力下降,影响日常活动和独立性.
  • 动力和力量有限会阻碍日常工作的能力,影响整体生活质量.

研究的目的:

  • 评估发达的上肢外骨架在老年人举重运动中的有效性.
  • 使用人体工程学分析和加权的K-最近邻居 (KNN) 机器学习算法来预测外骨的适用性.

主要方法:

  • 在使用设备之前和之后,对最大自愿同度收缩 (MVIC) 和平均功率频率 (MPF) 进行了实验测量.
  • 使用人体工程学分析和加权的KNN算法来评估肌肉力量和预测设备的有效性.
  • 老年人参与了举重任务,使用外骨或不使用外骨来测量肌肉反应.

主要成果:

  • 外骨的使用在5公斤和15公斤的举重中显著降低了%MVIC值.
  • 肌肉疲劳在双脚和 flexor carpi radialis 在没有外骨架的情况下增加,但随着外骨架的使用而减少.
  • %MVIC值在2-6% (没有负载),25-40% (5kg) 和30-71% (15kg) 之间.

结论:

关键词:
这是一个外骨架.上肢 外骨架 外骨架 上肢外骨架人体工程学是人体工程学.权重的K-最近邻居算法

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  • 发达的上肢外骨架有效地减少肌肉疲劳,并补偿在举重过程中老年人的力量损失.
  • 一个加权的KNN算法可以根据身体质量指数和肌肉疲劳水平来预测外骨的适用性.
  • 研究结果支持开发用户友好的辅助设备,突出了人体工程学和人工智能在增强康复技术中的作用.