重复提升运动预测 考虑到肌肉疲劳 肌肉疲劳
Yujiang Xiang1, Shuvrodeb Barman1, Ritwik Rakshit2,3
1School of Mechanical and Aerospace Engineering, Oklahoma State University, Stillwater, OK 74078.
Journal of biomechanical engineering
|April 11, 2025
概括
这项研究通过使用先进的模型预测肌肉疲劳,优化了重复的起重动作. 与三分区控制器 (3CC) 模型相比,增强恢复 (4CCr) 的四分区控制器模型准确地预测任务持续时间.
科学领域:
- 生物力学 生物力学
- 人体工程学就是人体工程学.
- 人类因素工程 人类因素工程
背景情况:
- 由于肌肉疲劳,重复举重任务存在风险.
- 准确预测任务持续时间对于预防受伤至关重要.
- 现有的肌肉疲劳模型在动态场景中存在局限性.
研究的目的:
- 为了预测重复举重任务的最佳运动,考虑到肌肉疲劳.
- 为了比较两个肌肉疲劳模型 (3CC和4CCr) 在预测举重任务持续时间方面的准确性.
- 将提升问题制定为优化任务,最大限度地减少动态力度和关节加速.
主要方法:
- 使用Denavit-Hartenberg (DH) 表示,利用了具有10个自由度 (DOF) 的2D数字人体模型.
- 采用基于关节的肌肉疲劳模型:三间隔控制器 (3CC) 和增强恢复的四间隔控制器 (4CCr).
- 制定了提升任务作为一个优化问题,并将关节角度配置 (四度B-splines) 和疲劳区控制点作为设计变量.
主要成果:
- 模拟生成了关节角度,扭矩和关节疲劳的配置文件,显示了明显的周期性模式.
- 数字模拟预测了3CC模型的11个提升周期和4CCr模型的13个周期,用于20公斤的箱子.
- 实验结果与4CCr模型对13次提升周期的预测一致.
结论:
- 4CCr肌肉疲劳模型在预测重复举重任务的持续时间方面,比3CC模型更准确.
- 根据疲劳预测优化运动可以提高提升任务的安全性和效率.
- 该研究为分析和预测人类在动态,重复性任务中的表现提供了强大的框架.
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