■校准的肌电图(EMG)驱动的肌肉骨骼模型可以提供对内部数量的深入了解(例如,肌肉力量)难以或不可能通过实验测量。然而,需要来自所有相关肌肉的EMG数据,这对EMG驱动建模方法的广泛应用构成了重大障碍.协同外推(SynX)是一种计算方法,可以在EMG驱动的模型校准过程中以合理的精度估计单个缺失的EMG信号,然而,其在估计更多缺失EMG信号方面的性能仍不清楚.
■这项研究评估了SynX可以使用八个测得的EMG信号来估计行走过程中同一腿中与八个缺失EMG信号相关的肌肉激活和力的准确性,同时进行EMG驱动的模型校准。从两个人中风后收集的实验步态数据,每条腿包括16个通道的EMG数据,用于校准EMG驱动的肌肉骨骼模型,为评估目的提供“黄金标准”肌肉活动和力量。然后使用SynX来预测与八个缺失的EMG信号相关的肌肉激活和力,同时校准EMG驱动的模型参数值。由于它的广泛使用,静态优化(SO)也被用来估计相同的肌肉激活和力量。使用均方根误差(RMSE)量化振幅误差和相关系数r值量化形状相似性来评估SynX和SO的估计精度。每个都是根据“黄金标准”肌肉活动和力量计算的。
■平均而言,SynX对未测量的肌肉激活产生了更准确的幅度和形状估计(RMSE0.08与0.15,r值0.55vs.0.12)和力(RMSE101.3N与174.4N,r值0.53vs.0.07)与SO相比。SynX产生了所有肌肉的校准的Hill型肌肉肌腱模型参数值和测量的肌肉的激活动力学模型参数值,这些参数值类似于“黄金标准”校准的模型参数值。
■这些发现表明,SynX可以通过少至八个精心选择的EMG信号来校准所有重要下肢肌肉的EMG驱动的肌肉骨骼模型,并最终有助于设计个性化康复和手术干预措施。
UNASSIGNED: Calibrated electromyography (EMG)-driven musculoskeletal models can provide great insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unclear.
UNASSIGNED: This study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing \"gold standard\" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to \"gold standard\" muscle activations and forces.
UNASSIGNED: On average, SynX produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15,r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N,r value 0.53 vs. 0.07) compared to SO. SynX yielded calibrated Hill-type muscle-tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to \"gold standard\" calibrated model parameter values.
UNASSIGNED: These findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.