Dickerson Clark R, Hughes Richard E, Chaffin Don B
Faculty of Applied Health Sciences, University of Waterloo, Kinesiology, Waterloo, ON, Canada.
Clin Biomech (Bristol). 2008 Aug;23(7):886-94. doi: 10.1016/j.clinbiomech.2008.04.004. Epub 2008 May 23.
Many evaluations of shoulder biomechanical models have focused on static exertions in constrained postures, but few have considered tasks that are more complex. This study examines model performance in load delivery tasks for a range of target locations.
The study evaluated an optimization-based muscle force prediction model used to assess dynamic load transfer tasks. Model predictions were compared with experimental electromyographic data for two task phases: (1) static hold and (2) dynamic reach.
Predictions correlated positively over all subjects with electromyographic data for prime movers (deltoid [r=0.53]; infraspinatus [r=0.63]; biceps [r=0.61]), though variations in the correlation existed across subjects and tasks. Conversely, the model predicted electromyographic activity of secondary muscles somewhat less accurately. The model also predicted inactivity for electromyographic inactive muscles.
The model provides important insights into activity levels muscles that most actively respond to external moments during manual load transfer tasks.
许多肩部生物力学模型评估都集中在受限姿势下的静态用力，但很少考虑更复杂的任务。本研究考察了一系列目标位置的负荷传递任务中的模型性能。
该研究评估了一个基于优化的肌肉力预测模型，用于评估动态负荷转移任务。将模型预测结果与两个任务阶段的实验肌电图数据进行比较：（1）静态保持和（2）动态伸展。
在所有受试者中，模型预测与原动肌（三角肌[r = 0.53]；冈下肌[r = 0.63]；肱二头肌[r = 0.61]）的肌电图数据呈正相关，尽管不同受试者和任务之间的相关性存在差异。相反，该模型对次要肌肉肌电图活动的预测准确性稍低。该模型还预测了肌电图无活动的肌肉的无活动状态。
该模型为手动负荷转移任务中最积极响应外部力矩的肌肉的活动水平提供了重要见解。
