Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization
Hybrid neuromusculoskeletal modeling to best track joint moments using a balance between muscle excitations derived from electromyograms and optimization
复制标题
DOI：
10.1016/j.jbiomech.2014.10.009
复制
发表时间：
2014-11-28
影响因子：
2.4
通讯作者：
Lloyd, David G.
中科院分区：
文献类型：
--
作者：
Sartori, Massimo;Farina, Dario;Lloyd, David G.
当前的肌电描记术（EMG）驱动的肌肉骨骼模型用于估计动态运动期间从个人四肢测量的关节力矩，其准确度各不相同。主要的好处是潜在的肌肉骨骼动力学模拟的功能，现实的，特定的主题，神经兴奋模式提供的EMG数据。主要缺点是表面EMG不能提供深层肌肉的信息。此外，EMG数据可能受到串扰、记录和后处理伪影的影响，这些伪影可能对EMG的信息内容产生不利影响。这限制了EMG驱动模型计算多肌肉动力学和关于多个自由度的所得关节力矩的能力。我们提出了一个混合神经肌肉骨骼模型，结合校准，主题特异性，肌电驱动和静态优化方法在一起。在此，通过平衡从实验EMG数据提取的信息内容和由静态优化方法产生的信息内容，使联合力矩跟踪误差最小化。使用5名健康男性受试者在步行和跑步过程中的运动数据，我们探索了混合模型的最佳配置，以最小限度地调整记录的EMG并预测缺失的EMG，同时实现对关节力矩的最佳跟踪。最小限度地调整和预测的激励大大提高了实验联合力矩跟踪精度比目前的肌电驱动模型。混合模型预测缺失肌肉肌电图的能力也进行了检查。所提出的混合模型，使肌肉驱动的模拟人体运动，同时强制执行肌肉兴奋模式的生理约束。这可能对研究肌电图记录有限的病理运动具有重要意义。(C)2014爱思唯尔有限公司版权所有。
Current electromyography (EMG)-driven musculoskeletal models are used to estimate joint moments measured from an individual's extremities during dynamic movement with varying levels of accuracy. The main benefit is the underlying musculoskeletal dynamics is simulated as a function of realistic, subject-specific, neural-excitation patterns provided by the EMG data. The main disadvantage is surface EMG cannot provide information on deeply located muscles. Furthermore, EMG data may be affected by cross-talk, recording and post-processing artifacts that could adversely influence the EMG's information content This limits the EMG-driven model's ability to calculate the multi-muscle dynamics and the resulting joint moments about multiple degrees of freedom. We present a hybrid neuromusculoskeletal model that combines calibration, subject-specificity, EMG-driven and static optimization methods together. In this, the joint moment tracking errors are minimized by balancing the information content extracted from the experimental EMG data and from that generated by a static optimization method. Using movement data from five healthy male subjects during walking and running we explored the hybrid model's best configuration to minimally adjust recorded EMGs and predict missing EMGs while attaining the best tracking of joint moments. Minimally adjusted and predicted excitations substantially improved the experimental joint moment tracking accuracy than current EMG-driven models. The ability of the hybrid model to predict missing muscle EMGs was also examined. The proposed hybrid model enables muscle-driven simulations of human movement while enforcing physiological constraints on muscle excitation patterns. This might have important implications for studying pathological movement for which EMG recordings are limited. (C) 2014 Elsevier Ltd. All rights reserved.