{"title":"比较协同外推法和静态优化法,以估计步行过程中多个未测量的肌肉激活。","authors":"Di Ao, Benjamin J Fregly","doi":"10.1186/s12984-024-01490-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"21 1","pages":"194"},"PeriodicalIF":5.2000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529311/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking.\",\"authors\":\"Di Ao, Benjamin J Fregly\",\"doi\":\"10.1186/s12984-024-01490-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>\",\"PeriodicalId\":16384,\"journal\":{\"name\":\"Journal of NeuroEngineering and Rehabilitation\",\"volume\":\"21 1\",\"pages\":\"194\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529311/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of NeuroEngineering and Rehabilitation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1186/s12984-024-01490-y\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of NeuroEngineering and Rehabilitation","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12984-024-01490-y","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
摘要
背景:校准肌电图(EMG)驱动的肌肉骨骼模型可以让人们深入了解难以或无法通过实验测量的内部数量(如肌肉力量)。然而,由于需要所有相关肌肉的肌电图数据,这对肌电图驱动建模方法的广泛应用构成了重大障碍。协同外推法(SynX)是一种计算方法,它能在 EMG 驱动模型校准过程中以合理的精度估算单个缺失的 EMG 信号,但它在估算大量缺失的 EMG 信号时的性能仍是未知数:本研究评估了 SynX 使用八个测量到的肌电信号估算同一条腿在行走过程中与八个缺失肌电信号相关的肌肉激活和力的准确性,同时进行肌电驱动模型校准。从两个中风后的人身上收集的实验步态数据(包括每条腿 16 个通道的 EMG 数据)被用于校准 EMG 驱动的肌肉骨骼模型,为评估目的提供 "黄金标准 "肌肉激活和力量。然后使用 SynX 预测与八个缺失的 EMG 信号相关的肌肉激活和力量,同时校准 EMG 驱动的模型参数值。由于静态优化(SO)的广泛应用,它也被用于按比例通用肌肉骨骼模型,以估算相同的肌肉激活度和力。使用均方根误差(RMSE)来量化振幅误差,使用相关系数 r 值来量化形状相似性,评估 SynX 和 SO 的估计精度,每种精度都是根据 "黄金标准 "肌肉激活度和力计算得出的:平均而言,与 SO 相比,同步校准模型的 SynX 对未测量的肌肉激活(RMSE 0.08 vs. 0.15,r 值 0.55 vs. 0.12)和力(RMSE 101.3 N vs. 174.4 N,r 值 0.53 vs. 0.07)产生的振幅和形状估计更准确。SynX 校准了所有肌肉的希尔型肌肉-肌腱模型参数值,测量肌肉的激活动态模型参数值与 "金标准 "校准模型参数值相似:这些研究结果表明,SynX 可以为所有重要的下肢肌肉校准 EMG 驱动的肌肉骨骼模型,只需精心选择 8 个 EMG 信号,并最终为设计针对行动障碍的个性化康复和手术干预措施做出贡献。
Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking.
Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
期刊介绍:
Journal of NeuroEngineering and Rehabilitation considers manuscripts on all aspects of research that result from cross-fertilization of the fields of neuroscience, biomedical engineering, and physical medicine & rehabilitation.