通过肌肉骨运动模拟,从虚拟惯性传感器数据中估计3D动力学和动力学
Marlies Nitschke1, Eva Dorschky1, Sigrid Leyendecker2
1Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Frontiers in bioengineering and biotechnology
|April 17, 2024
概括
本研究使用最佳控制模拟与惯性传感器数据来准确重建3D运行动力学和动力学. 该方法确保了生物力学分析的动态一致性,为伤害预防研究带来了希望.
科学领域:
- 生物力学 生物力学
- 动作捕捉是一种动作捕捉技术.
- 肌肉骨模型的建模
- 最佳控制控制的最佳方式
背景情况:
- 便携式惯性传感器系统允许在实验室外进行大规模研究的运动捕获.
- 从惯性数据准确的3D动力学和动力学估计仍然是一个挑战.
- 现有的方法可能会在动力学和动力学之间引入不一致.
研究的目的:
- 通过使用最佳控制模拟来研究从惯性传感器数据中重建3D动力学和动力学.
- 评估这种方法对任意跑动的可行性.
- 确保动态一致的生物力学分析,用于诸如伤害预防等应用.
主要方法:
- 开发了全身肌肉骨模型的最佳控制模拟.
- 从模拟的惯性数据中制定了追踪虚拟加速和角速度的问题.
- 通过从光学运动捕捉数据中获得的标记跟踪模拟来验证该方法.
主要成果:
- 最佳控制方法密切跟踪惯性数据,实现关键生物力学变量的低根平均平方偏差.
- 对所有重建的生物力学变量观察到高的多重相关系数.
- 该方法成功地重建了各种跑步运动 (直,曲,V切) 的动力学和动力学,并保持了动态一致性.
结论:
- 追踪3D惯性数据的最佳控制模拟可以准确地重建运行运动的动力学和动力学.
- 这种方法产生了相互和动态一致的结果,使因果链研究 (例如,ACL伤害预防) 成为可能.
- 这种方法对于使用惯性传感器数据进行全面的生物力学分析非常有希望,尽管在现实世界测量方面存在潜在的挑战.
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