An open-source model and solution method to predict co-contraction in the finger

A novel open-source biomechanical model of the index finger with an electromyography (EMG)-constrained static optimization solution method are developed with the goal of improving co-contraction estimates and providing means to assess tendon tension distribution through the finger. The Intrinsic model has four degrees of freedom and seven muscles (with a 14 component extensor mechanism). A novel plugin developed for the OpenSim modelling software applied the EMG-constrained static optimization solution method. Ten participants performed static pressing in three finger postures and five dynamic free motion tasks. Index finger 3D kinematics, force (5, 15, 30 N), and EMG (4 extrinsic muscles and first dorsal interosseous) were used in the analysis. The Intrinsic model predicted co-contraction increased by 29% during static pressing over the existing model. Further, tendon tension distribution patterns and forces, known to be essential to produce finger action, were determined by the model across all postures. The Intrinsic model and custom solution method improved co-contraction estimates to facilitate force propagation through the finger. These tools improve our interpretation of loads in the finger to develop better rehabilitation and workplace injury risk reduction strategies.

本研究开发了一种新颖的开源指骨生物力学模型，并采用肌电图（EMG）约束下的静态优化解决方案方法，旨在提高协同收缩的估计精度，并为通过手指评估肌腱张力分布提供手段。该内源模型具有四个自由度，包含七个肌肉（带有14个成分的伸肌机制）。为OpenSim建模软件开发的插件应用了EMG约束下的静态优化解决方案方法。十名参与者进行了三种手指姿态的静态按压和五种动态自由运动任务。在分析中使用了手指的3D运动学、力（5N、15N、30N）以及肌电图（四个外在肌肉和第一背侧骨间肌）。内源模型预测，在静态按压过程中，协同收缩相较于现有模型增加了29%。此外，模型确定了所有姿态下肌腱张力分布模式和力，这些因素对于产生手指动作至关重要。内源模型和定制解决方案方法提高了协同收缩估计，以促进力通过手指的传播。这些工具改进了我们对手指负荷的解释，有助于开发更有效的康复策略和工作场所伤害风险降低措施。