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人体运动中肌肉力量估计不同方法的比较。

Comparison of different methods for estimating muscle forces in human movement.

作者信息

Lin Yi-Chung, Dorn Tim W, Schache Anthony G, Pandy Marcus G

机构信息

Department of Mechanical Engineering, University of Melbourne, Parkville, Victoria, Australia.

出版信息

Proc Inst Mech Eng H. 2012 Feb;226(2):103-12. doi: 10.1177/0954411911429401.

Abstract

The aim of this study was to compare muscle-force estimates derived for human locomotion using three different methods commonly reported in the literature: static optimisation (SO), computed muscle control (CMC) and neuromusculoskeletal tracking (NMT). In contrast with SO, CMC and NMT calculate muscle forces dynamically by including muscle activation dynamics. Furthermore, NMT utilises a time-dependent performance criterion, wherein a single optimisation problem is solved over the entire time interval of the task. Each of these methods was used in conjunction with musculoskeletal modelling and experimental gait data to determine lower-limb muscle forces for self-selected speeds of walking and running. Correlation analyses were performed for each muscle to quantify differences between the various muscle-force solutions. The patterns of muscle loading predicted by the three methods were similar for both walking and running. The correlation coefficient between any two sets of muscle-force solutions ranged from 0.46 to 0.99 (p < 0.001 for all muscles). These results suggest that the robustness and efficiency of static optimisation make it the most attractive method for estimating muscle forces in human locomotion.

摘要

本研究的目的是比较文献中常见的三种不同方法得出的人体运动肌肉力估计值

静态优化(SO)、计算肌肉控制(CMC)和神经肌肉骨骼跟踪(NMT)。与SO不同,CMC和NMT通过纳入肌肉激活动力学来动态计算肌肉力。此外,NMT采用时间相关的性能标准,即在任务的整个时间间隔内求解单个优化问题。这些方法中的每一种都与肌肉骨骼建模和实验步态数据结合使用,以确定自选步行和跑步速度下的下肢肌肉力。对每块肌肉进行相关性分析,以量化各种肌肉力解决方案之间的差异。三种方法预测的步行和跑步肌肉负荷模式相似。任意两组肌肉力解决方案之间的相关系数在0.46至0.99之间(所有肌肉的p<0.001)。这些结果表明,静态优化的稳健性和效率使其成为估计人体运动肌肉力最具吸引力的方法。

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