UNC-NC State Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC 27695, United States; UNC-NC State Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, United States.
J Biomech. 2022 Aug;141:111200. doi: 10.1016/j.jbiomech.2022.111200. Epub 2022 Jun 16.
EMG-driven neuromusculoskeletal models have been used to study many impairments and hold great potential to facilitate human-machine interactions for rehabilitation. A challenge to successful clinical application is the need to optimize the model parameters to produce accurate kinematic predictions. In order to identify the key parameters, we used Monte-Carlo simulations to evaluate the sensitivities of wrist and metacarpophalangeal (MCP) flexion/extension prediction accuracies for an EMG-driven, lumped-parameter musculoskeletal model. Four muscles were modeled with 22 total optimizable parameters. Model predictions from EMG were compared with measured joint angles from 11 able-bodied subjects. While sensitivities varied by muscle, we determined muscle moment arms, maximum isometric force, and tendon slack length were highly influential, while passive stiffness and optimal fiber length were less influential. Removing the two least influential parameters from each muscle reduced the optimization search space from 22 to 14 parameters without significantly impacting prediction correlation (wrist: 0.90 ± 0.05 vs 0.90 ± 0.05, p = 0.96; MCP: 0.74 ± 0.20 vs 0.70 ± 0.23, p = 0.51) and normalized root mean square error (wrist: 0.18 ± 0.03 vs 0.19 ± 0.03, p = 0.16; MCP: 0.18 ± 0.06 vs 0.19 ± 0.06, p = 0.60). Additionally, we showed that wrist kinematic predictions were insensitive to parameters of the modeled MCP muscles. This allowed us to develop a novel optimization strategy that more reliably identified the optimal set of parameters for each subject (27.3 ± 19.5%) compared to the baseline optimization strategy (6.4 ± 8.1%; p = 0.004). This study demonstrated how sensitivity analyses can be used to guide model refinement and inform novel and improved optimization strategies, facilitating implementation of musculoskeletal models for clinical applications.
肌电图驱动的神经肌肉骨骼模型已被用于研究许多损伤，并具有促进康复人机交互的巨大潜力。成功临床应用的一个挑战是需要优化模型参数以产生准确的运动学预测。为了确定关键参数，我们使用蒙特卡罗模拟来评估肌电图驱动的集中参数神经肌肉骨骼模型对手腕和掌指（MCP）弯曲/伸展预测精度的灵敏度。四个肌肉用 22 个总可优化参数进行建模。将肌电图模型的预测与 11 个健康受试者的测量关节角度进行比较。虽然灵敏度因肌肉而异，但我们确定肌肉力矩臂、最大等长力和肌腱松弛长度的影响较大，而被动刚度和最佳纤维长度的影响较小。从每个肌肉中去除两个最不影响的参数，将优化搜索空间从 22 个参数减少到 14 个参数，而不会显著影响预测相关性（手腕：0.90 ± 0.05 对 0.90 ± 0.05，p = 0.96；MCP：0.74 ± 0.20 对 0.70 ± 0.23，p = 0.51）和归一化均方根误差（手腕：0.18 ± 0.03 对 0.19 ± 0.03，p = 0.16；MCP：0.18 ± 0.06 对 0.19 ± 0.06，p = 0.60）。此外，我们还表明，手腕运动学预测对所建模的 MCP 肌肉的参数不敏感。这使我们能够开发一种新的优化策略，该策略比基线优化策略（6.4 ± 8.1%；p = 0.004）更可靠地确定每个受试者的最佳参数集（27.3 ± 19.5%）。本研究表明，敏感性分析如何可用于指导模型改进，并为新型和改进的优化策略提供信息，从而促进神经肌肉骨骼模型在临床应用中的实施。
