Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
Department for Mechanical Engineering, Rice University, Houston, TX, USA.
J Neuroeng Rehabil. 2024 Nov 1;21(1):194. doi: 10.1186/s12984-024-01490-y.
Calibrated 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.
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) 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 coefficient r values to quantify shape similarity, each calculated with respect to "gold standard" muscle activations and forces.
On 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, r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N, r value 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.
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.
校准后的肌电图（EMG）驱动的肌肉骨骼模型可以深入了解内部参数（例如肌肉力量），这些参数很难或不可能通过实验测量。然而，需要所有相关肌肉的 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，它可以在 EMG 驱动模型校准过程中以合理的精度估计单个缺失的 EMG 信号，但它在估计更多缺失的 EMG 信号方面的性能仍不清楚。
本研究评估了 SynX 使用 8 个测量的 EMG 信号来估计步行时同一条腿中 8 个缺失的 EMG 信号相关的肌肉激活和力的准确性，同时进行 EMG 驱动模型校准。使用从两名中风后患者采集的 16 通道腿部 EMG 数据来校准 EMG 驱动的肌肉骨骼模型，为评估目的提供“黄金标准”肌肉激活和力。然后，SynX 用于预测与八个缺失的 EMG 信号相关的肌肉激活和力，同时校准 EMG 驱动模型参数值。由于其广泛使用，还应用了静态优化（SO）应用于比例通用肌肉骨骼模型来估计相同的肌肉激活和力。使用均方根误差（RMSE）来量化幅度误差和相关系数 r 值来量化形状相似性，分别相对于“黄金标准”肌肉激活和力进行评估，以评估 SynX 和 SO 的估计准确性。
与 SO 相比，同步模型校准的 SynX 产生的未测量肌肉激活的幅度和形状估计明显更准确（RMSE 分别为 0.08 和 0.15，r 值分别为 0.55 和 0.12）和力（RMSE 分别为 101.3 N 和 174.4 N，r 值分别为 0.53 和 0.07）。SynX 为所有肌肉生成了校准的 Hill 型肌肉肌腱模型参数值，为测量肌肉生成了激活动力学模型参数值，这些参数值与“黄金标准”校准模型参数值相似。
这些发现表明，SynX 可以使用尽可能少的 8 个精心选择的 EMG 信号来校准 EMG 驱动的肌肉骨骼模型，用于所有重要的下肢肌肉，并最终有助于设计个性化的康复和手术干预措施，以改善运动障碍。
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