Comparison of Synergy Extrapolation and Static Optimization for Estimating Multiple Unmeasured Muscle Activations during Walking.
Comparison of Synergy Extrapolation and Static Optimization for Estimating Multiple Unmeasured Muscle Activations during Walking.
复制标题
用于估计步行期间多个未测量的肌肉激活的协同外推法和静态优化的比较。
DOI：
10.1101/2024.03.03.583228
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Benjamin,JFregly
中科院分区：
文献类型：
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作者：
Di,Ao;Benjamin,JFregly
校准的肌电图（EMG）驱动的肌肉骨骼模型可以提供对难以或不可能通过实验测量的内部数量（例如肌肉力）的洞察。然而，需要所有受累肌肉的肌电信号数据，这对肌电信号驱动的建模方法的广泛应用构成了重大障碍。协同外推法（SynX）是一种在肌电信号驱动的模型校准过程中能够以合理的精度估计单个缺失肌电信号的计算方法，但其在估计大量缺失肌电信号方面的性能尚不清楚。本研究评估了SynX在行走过程中使用8个测量到的肌电信号来估计与8个缺失的肌电信号相关的肌肉激活和力的准确性，同时进行肌电信号驱动的模型校准。从两名中风患者身上收集的实验步态数据，包括每条腿16个通道的肌电信号数据，用于校准肌电信号驱动的肌肉骨骼模型，为评估目的提供“金标准”肌肉激活和力。然后使用SynX预测与8个缺失肌电信号相关的肌肉激活和力，同时校准肌电信号驱动的模型参数值。由于它的广泛应用，静态优化（SO）应用于缩放的通用肌肉骨骼模型也被用来估计相同的肌肉激活和力。使用均方根误差（RMSE）来量化幅度误差，使用相关系数值来量化形状相似性，分别根据“黄金标准”肌肉激活和力来计算SynX和SO的估计精度。结果平均而言，与SO相比，同步模型校准的SynX对未测量的肌肉激活（RMSE 0.08 vs. 0.15，右值0.55 vs. 0.12）和力（RMSE 101.3 N vs. 174.4 N，右值0.53 vs. 0.07）产生了更准确的幅度和形状估计。SynX计算出所有肌肉的校正Hill-type肌肉肌腱模型参数值和测量肌肉的激活动力学模型参数值，与“金标准”校正模型参数值相似。这些发现表明，SynX可以通过8个精心挑选的肌电信号来校准所有重要下肢肌肉的肌电驱动肌肉骨骼模型，并最终有助于设计个性化康复和手术干预行动障碍。
BackgroundCalibrated electromyography (EMG)-driven musculoskeletal models can provide 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 unknown.MethodsThis 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) applied to a scaled generic musculoskeletal model 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 coefficientrvalues to quantify shape similarity, each calculated with respect to “gold standard” muscle activations and forces.ResultsOn average, compared to SO, SynX with simultaneous model calibration produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15,rvalue 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N,rvalue 0.53 vs. 0.07). 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.ConclusionsThese 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.