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将基于图像的体内实验数据纳入希尔型肌肉模型会影响行走过程中个体力分配策略的估计。

Inclusion of image-based in vivo experimental data into the Hill-type muscle model affects the estimation of individual force-sharing strategies during walking.

作者信息

Hamard Raphaël, Hug François, Kelp Nicole Y, Feigean Romain, Aeles Jeroen, Dick Taylor J M

机构信息

Nantes Université, Movement - Interactions - Performance, MIP, UR 4334, F-44000 Nantes, France.

Nantes Université, Movement - Interactions - Performance, MIP, UR 4334, F-44000 Nantes, France; The University of Queensland, School of Biomedical Sciences, Brisbane, Queensland, Australia; Institut Universitaire de France (IUF), Paris, France; Université Côte d'Azur, LAMHESS, Nice, France.

出版信息

J Biomech. 2022 Apr;135:111033. doi: 10.1016/j.jbiomech.2022.111033. Epub 2022 Mar 4.

Abstract

The study of muscle coordination requires knowledge of the force produced by individual muscles, which can be estimated using Hill-type models. Predicted forces from Hill-type models are sensitive to the muscle's maximal force-generating capacity (F), however, to our knowledge, no study has investigated the effect of different F personalization methods on predicted muscle forces. The aim of this study was to determine the influence of two personalization methods on predicted force-sharing strategies between the human gastrocnemii during walking. Twelve participants performed a walking protocol where we estimated muscle activation using surface electromyography and fascicle length, velocity, and pennation angle using B-mode ultrasound to inform the Hill-type model. F was determined using either a scaling method or experimental method. The scaling method used anthropometric scaling to determine both muscle volume and fiber length, which were used to estimate the F of the gastrocnemius medialis and lateralis. The experimental method used muscle volume and fascicle length obtained from magnetic resonance imaging and diffusion tensor imaging, respectively. We found that the scaling and the experimental method predicted similar gastrocnemii force-sharing strategies at the group level (mean over the participants). However, substantial differences between methods in predicted force-sharing strategies was apparent for some participants revealing the limited ability of the scaling method to predict force-sharing strategies at the level of individual participants. Further personalization of muscle models using in vivo experimental data from imaging techniques is therefore likely important when using force predictions to inform the diagnosis and management of neurological and orthopedic conditions.

摘要

肌肉协调的研究需要了解单个肌肉产生的力,这可以使用希尔型模型进行估计。然而,据我们所知,希尔型模型预测的力对肌肉的最大力产生能力(F)很敏感,尚无研究调查不同F个性化方法对预测肌肉力的影响。本研究的目的是确定两种个性化方法对步行过程中人类腓肠肌之间预测的力分配策略的影响。12名参与者执行了一项步行方案,我们使用表面肌电图估计肌肉激活,并使用B型超声估计肌束长度、速度和羽状角,以告知希尔型模型。F通过缩放方法或实验方法确定。缩放方法使用人体测量缩放来确定肌肉体积和纤维长度,用于估计内侧腓肠肌和外侧腓肠肌的F。实验方法分别使用从磁共振成像和扩散张量成像获得的肌肉体积和肌束长度。我们发现,在组水平(参与者的平均值)上,缩放方法和实验方法预测的腓肠肌力分配策略相似。然而,对于一些参与者来说,两种方法在预测力分配策略上存在明显差异,这表明缩放方法在个体参与者水平上预测力分配策略的能力有限。因此,当使用力预测来指导神经和骨科疾病的诊断和管理时,使用来自成像技术的体内实验数据对肌肉模型进行进一步个性化可能很重要。

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