Princelle Domitille, Viceconti Marco, Davico Giorgio
Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy.
Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
Ann Biomed Eng. 2025 Jun;53(6):1399-1408. doi: 10.1007/s10439-025-03713-2. Epub 2025 Mar 24.
Personalized musculoskeletal models are crucial to get insights into the mechanisms underpinning neuromusculoskeletal disorders and have the potential to support clinicians in the daily management and evaluation of patients. However, their use is still limited due to the lack of validation studies, which hinders people's trust in these technologies. The current study aims to assess the predictive accuracy of two common approaches to estimate knee joint contact forces, when employing musculoskeletal models.
Subject-specific musculoskeletal models were developed for four elderly subjects, exploiting the freely accessible Knee Grand Challenge datasets, and used to perform biomechanical simulations of level walking to estimate knee joint contact forces. The classical static optimization and EMG-assisted approaches were implemented to resolve the muscle redundancy problem. Their estimates were compared, in terms of predictive accuracy, against the experimental recordings from an instrumented knee implant and against one another. Spatiotemporal differences were identified through Statistical Parametrical Mapping, to complement traditional similarity metrics (R, RMSE, 95th percentile, and the maximal error).
Both methods allowed to estimate the experimental knee joint contact forces experienced during walking with a high level of accuracy (R > 0.82, RMSE < 0.56 BW). The EMG-assisted approach further enabled to highlight subject-specific features that were not captured otherwise, such as a prolonged or anticipated muscle-co-contraction.
While the static optimization approach provides reasonable estimates for subjects exhibiting typical gait, the EMG-assisted approach should be preferred and employed when studying clinical populations or patients exhibiting abnormal walking patterns.
个性化肌肉骨骼模型对于深入了解神经肌肉骨骼疾病的发病机制至关重要，并且有可能在患者的日常管理和评估中为临床医生提供支持。然而，由于缺乏验证研究，它们的应用仍然有限，这阻碍了人们对这些技术的信任。本研究旨在评估在使用肌肉骨骼模型时，两种常见的估计膝关节接触力方法的预测准确性。
利用可免费获取的膝关节大挑战数据集，为四名老年受试者建立了特定于个体的肌肉骨骼模型，并用于进行平地行走的生物力学模拟，以估计膝关节接触力。采用经典的静态优化和肌电图辅助方法来解决肌肉冗余问题。将它们的估计值在预测准确性方面与仪器化膝关节植入物的实验记录以及相互之间进行比较。通过统计参数映射确定时空差异，以补充传统的相似性指标（R、RMSE、第95百分位数和最大误差）。
两种方法都能够以较高的准确性估计行走过程中实验性膝关节接触力（R > 0.82，RMSE < 0.56 BW）。肌电图辅助方法还能够突出显示其他方法未捕捉到的个体特异性特征，例如延长的或预期的肌肉共同收缩。
虽然静态优化方法为表现出典型步态的受试者提供了合理的估计，但在研究临床人群或表现出异常行走模式的患者时，应优先选择并采用肌电图辅助方法。
