ISM UMR 7287, Aix-Marseille University, CNRS, Marseille, France.
Department of Bioengineering, Imperial College London, London, UK.
Med Biol Eng Comput. 2017 Dec;55(12):2227-2244. doi: 10.1007/s11517-017-1662-6. Epub 2017 Jun 19.
Because the force-generating capacities of muscles are currently estimated using anatomical data obtained from cadaver specimens, hand musculoskeletal models provide only a limited representation of the specific features of individual subjects. A scaling method is proposed to individualise muscle capacities using dynamometric measurements and electromyography. For each subject, a strength profile was first defined by measuring net moments during eight maximum isometric contractions about the wrist and metacarpophalangeal joints. The capacities of the five muscle groups were then determined by adjusting several parameters of an initial musculoskeletal model using an optimisation procedure which minimised the differences between measured moments and model estimates. Sixteen volunteers, including three particular participants (one climber, one boxer and one arthritic patient), were recruited. Compared with the initial literature-based model, the estimated subject-specific capacities were on average five times higher for the wrist muscles and twice as high for the finger muscles. The adjustments for particular subjects were consistent with their expected specific characteristics, e.g. high finger flexor capacities for the climber. Using the subject-specific capacities, the model estimates were markedly modified. The proposed protocol and scaling procedure can capture the specific characteristics of the participants and improved the representation of their capacities in the musculoskeletal model.
由于肌肉的产生力量的能力目前是使用从尸体标本获得的解剖学数据来估计的，手部肌肉骨骼模型仅能提供个体受试者特定特征的有限表示。提出了一种使用测力和肌电图对肌肉能力进行个体化的缩放方法。对于每个受试者，首先通过测量腕关节和掌指关节的 8 次最大等长收缩期间的净力矩来定义强度曲线。然后，通过使用最小化测量力矩和模型估计之间差异的优化程序调整初始肌肉骨骼模型的几个参数来确定五个肌肉群的能力。招募了 16 名志愿者，其中包括三名特殊参与者（一名攀岩者、一名拳击手和一名关节炎患者）。与初始基于文献的模型相比，估计的受试者特定能力对于腕部肌肉平均高 5 倍，对于手指肌肉高 2 倍。对于特定受试者的调整与他们预期的特定特征一致，例如攀岩者的手指屈肌能力较高。使用受试者特定的能力，模型估计得到了明显的修改。所提出的协议和缩放程序可以捕获参与者的特定特征，并改善肌肉骨骼模型中对其能力的表示。
