University of Massachusetts Amherst, Department of Kinesiology, 110 Totman Building, 30 Eastman Lane, Amherst, MA 01003, United States; Beth Israel Deaconess Medical Center, Center for Advanced Orthopaedic Studies, 330 Brookline Avenue, RN 115, Boston, MA 02215, United States; Harvard Medical School, Department of Orthopaedic Surgery, Boston, MA 02115, United States.
University of Michigan, School of Kinesiology, 830 North University Avenue, Ann Arbor, MI 48109, United States.
Med Eng Phys. 2022 May;103:103790. doi: 10.1016/j.medengphy.2022.103790. Epub 2022 Mar 18.
Participant-specific musculoskeletal models are needed to accurately estimate lower back internal kinetic demands and injury risk. In this study we developed the framework for incorporating an electromyography optimization (EMGopt) approach within OpenSim (https://simtk.org/projects/emg_opt_tool) and evaluated lower back demands estimated from the model during gait. Kinematic, external kinetic, and EMG data were recorded from six participants as they performed walking and carrying tasks on a treadmill. For evaluation, predicted lumbar vertebral joint forces were compared to those from a generic static optimization approach (SOpt) and to previous studies. Further, model-estimated muscle activations were compared to recorded EMG, and model sensitivity to day-to-day EMG variability was evaluated. Results showed the vertebral joint forces from the model were qualitatively similar in pattern and magnitude to literature reports. Compared to SOpt, the EMGopt approach predicted larger joint loads (p<.01) with muscle activations better matching individual participant EMG patterns. L5/S1 vertebral joint forces from EMGopt were sensitive to the expected variability of recorded EMG, but the magnitude of these differences (±4%) did not impact between-task comparisons. Despite limitations inherent to such models, the proposed musculoskeletal model and EMGopt approach appears well-suited for evaluating internal lower back demands during gait tasks.
参与者特异性肌肉骨骼模型对于准确估计下背部内部动力学需求和损伤风险至关重要。本研究我们在 OpenSim 中（https://simtk.org/projects/emg_opt_tool）开发了纳入肌电图优化（EMGopt）方法的框架，并评估了模型在步态期间估计的下背部需求。当参与者在跑步机上进行行走和携带任务时，记录了他们的运动学、外部动力学和肌电图数据。为了进行评估，预测的腰椎关节力与通用静态优化方法（SOpt）和先前的研究进行了比较。此外，还比较了模型估计的肌肉激活与记录的肌电图，并评估了模型对日常肌电图变异性的敏感性。结果表明，模型预测的腰椎关节力在模式和幅度上与文献报道的定性相似。与 SOpt 相比，EMGopt 方法预测的关节负荷更大（p<.01），肌肉激活更符合个体参与者的肌电图模式。来自 EMGopt 的 L5/S1 腰椎关节力对记录肌电图的预期变异性敏感，但这些差异的幅度（±4%）不会影响任务间比较。尽管此类模型存在固有局限性，但提出的肌肉骨骼模型和 EMGopt 方法似乎非常适合评估步态任务期间下背部的内部需求。
