Alvim Felipe Costa, Lucareli Paulo Roberto Garcia, Menegaldo Luciano Luporini
Biomedical Engineering Program, COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Department of Rehabilitation Science, Human Motion Analysis Laboratory, Universidade Nove de Julho, São Paulo, Brazil.
Gait Posture. 2018 Jan;59:298-303. doi: 10.1016/j.gaitpost.2017.07.038. Epub 2017 Jul 15.
Functional biomechanical tests allow the assessment of musculoskeletal system impairments in a simple way. Muscle force synergies associated with movement can provide additional information for diagnosis. However, such forces cannot be directly measured noninvasively. This study aims to estimate muscle activations and forces exerted during the preparation phase of the single leg triple hop test. Two different approaches were tested: static optimization (SO) and computed muscle control (CMC). As an indirect validation, model-estimated muscle activations were compared with surface electromyography (EMG) of selected hip and thigh muscles. Ten physically healthy active women performed a series of jumps, and ground reaction forces, kinematics and EMG data were recorded. An existing OpenSim model with 92 musculotendon actuators was used to estimate muscle forces. Reflective markers data were processed using the OpenSim Inverse Kinematics tool. Residual Reduction Algorithm (RRA) was applied recursively before running the SO and CMC. For both, the same adjusted kinematics were used as inputs. Both approaches presented similar residuals amplitudes. SO showed a closer agreement between the estimated activations and the EMGs of some muscles. Due to inherent EMG methodological limitations, the superiority of SO in relation to CMC can be only hypothesized. It should be confirmed by conducting further studies comparing joint contact forces. The workflow presented in this study can be used to estimate muscle forces during the preparation phase of the single leg triple hop test and allows investigating muscle activation and coordination.
功能生物力学测试能够以简单的方式评估肌肉骨骼系统损伤情况。与运动相关的肌肉力量协同作用可为诊断提供额外信息。然而,此类力量无法通过非侵入性方式直接测量。本研究旨在估计单腿三级跳测试准备阶段所施加的肌肉激活情况和力量。测试了两种不同方法:静态优化(SO)和计算肌肉控制(CMC)。作为间接验证,将模型估计的肌肉激活情况与选定的髋部和大腿肌肉的表面肌电图(EMG)进行了比较。十名身体健康的活跃女性进行了一系列跳跃,并记录了地面反作用力、运动学和肌电图数据。使用具有92个肌肉肌腱驱动装置的现有OpenSim模型来估计肌肉力量。使用OpenSim逆运动学工具处理反光标记数据。在运行SO和CMC之前,递归应用残差减少算法(RRA)。对于这两种方法,均使用相同的调整后的运动学作为输入。两种方法呈现出相似的残差幅度。SO在一些肌肉的估计激活情况和肌电图之间显示出更紧密的一致性。由于肌电图方法存在固有局限性,只能推测SO相对于CMC的优越性。应通过开展进一步比较关节接触力的研究来加以证实。本研究中呈现的工作流程可用于估计单腿三级跳测试准备阶段的肌肉力量,并有助于研究肌肉激活和协调性。