肩部建模中最先进肌肉募集策略的基准和验证

IF 2.6 2区 工程技术 Q2 MECHANICS
Maxence Lavaill, Claudio Pizzolato, Bart Bolsterlee, Saulo Martelli, Peter Pivonka
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引用次数: 0

摘要

通过建模估算出的肩部肌肉力量通常是通过盂肱关节反作用力(GHJ-RF)的测量结果间接验证的。本验证研究根据公开的 GHJ-RF 测量结果,对几种肌肉招募策略的结果进行了基准测试。研究人员从一名经过挑选的男性参与者处获得了运动学、肌电图和 GHJ-RF 数据,该参与者在执行 2.4 千克重的肩关节外展任务时,GHJ 高度可达 92°。代尔夫特肩关节和肘关节模型根据该参与者的情况进行了缩放。通过以下方法解决肌肉招募问题:1)最小化肌肉激活平方(SO);2)考虑肌肉动态特性(CMC);3)根据相应的表面肌电图测量值(CEINMS)限制肌肉兴奋。此外,还通过马尔可夫链蒙特卡洛随机抽样确定了模型中可接受的 GHJ-RF 频谱。实验的 GHJ-RF 与不同肌肉募集策略的结果 GHJ-RF 以及可容许的随机范围进行了比较。从肱骨抬高 21 度到 40 度,GHJ-RF 的实验测量值超出了模型的容许范围(体重 (%BW) 的 21% 到 659%)。关节力均方误差介于 21%(SO)和 24%(CEINMS)之间。在高仰角时,CMC(11%BW)和 CEINMS(14%BW)的表现优于 SO(25%BW)。这为最佳选择肌肉募集策略提供了指南。在高仰角时,CMC 和 CEINMS 是预测 GHJ-RF 最准确的两种方法。SO 在低仰角时表现最佳。此外,随机肌肉取样凸显了模型与低仰角实验数据之间缺乏一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Benchmark and validation of state-of-the-art muscle recruitment strategies in shoulder modelling

Benchmark and validation of state-of-the-art muscle recruitment strategies in shoulder modelling

Shoulder muscle forces estimated via modelling are typically indirectly validated against measurements of glenohumeral joint reaction forces (GHJ-RF). This validation study benchmarks the outcomes of several muscle recruitment strategies against public GHJ-RF measurements. Public kinematics, electromyography, and GHJ-RF data from a selected male participant executing a 2.4 kg weight shoulder abduction task up to 92° GHJ elevation were obtained. The Delft Shoulder and Elbow Model was scaled to the participant. Muscle recruitment was solved by 1) minimising muscle activations squared (SO), 2) accounting for dynamic muscle properties (CMC) and 3) constraining muscle excitations to corresponding surface electromyography measurements (CEINMS). Moreover, the spectrum of admissible GHJ-RF in the model was determined via Markov-chain Monte Carlo stochastic sampling. The experimental GHJ-RF was compared to the resultant GHJ-RF of the different muscle recruitment strategies as well as the admissible stochastic range. From 21 to 40 degrees of humeral elevation, the experimental measurement of the GHJ-RF was outside the admissible range of the model (21 to 659% of body weight (%BW)). Joint force RMSE was between 21 (SO) and 24%BW (CEINMS). At high elevation angles, CMC (11%BW) and CEINMS (14%BW) performed better than SO (25%BW). A guide has been proposed to best select muscle recruitment strategies. At high elevation angles, CMC and CEINMS were the two most accurate methods in terms of predicted GHJ-RF. SO performed best at low elevation angles. In addition, stochastic muscle sampling highlighted the lack of consistency between the model and experimental data at low elevation angles.

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来源期刊
CiteScore
6.00
自引率
17.60%
发文量
46
审稿时长
12 months
期刊介绍: The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations. The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.
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