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A validation study on muscle activity prediction of a lower limb musculoskeletal model using EMG during normal walking

机译:肌电图预测正常行走过程中下肢肌肉骨骼模型肌肉活动的验证研究

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This study focused on validating muscle activities predicted by the a Lower Limb musculoskeletal model of AnyBody Modeling System (AMS) against measured muscle activity (EMG) from ten healthy subjects who performed a normal walking task. The GaitLowerExtremity Model (GLEM) of AMS was used in this study. Eight EMG electrodes measured the activity of eight muscles of the right leg: Vastus Medialis, Vastus Lateralis, Rectus Femoris, Semitendinosus, Biceps Femoris, Gastrocnemius Medialis and Lateralis and Tibialis Anterior. Four different thresholds were applied on both curves (predicted and measured muscle activity): 10%, 25%, 35% and 45% of the mean of the graph threshold (MGT) before they were being compared quantitatively in the same threshold level. Number of onset and offset of muscle activity were used to quantify the level of agreement. Visual inspection showed good agreement between EMG and predicted muscle activity. In general, for all parameters the 45 % MGT showed the best agreement compared to the other MGT. For the number of onset and offset, two muscles showed fair agreement and four muscles showed slight agreement. This first attempt in a quantitative point of view showed that 6 muscles showed a slight positive agreement out of 8 in both variables number of onset and offset. The differences between AMS and EMG patterns can be attributed to the nature of the modeling process such as assumption and simplification, and possible inaccuracy of determining knee kinetic data during motion.
机译:这项研究的重点是验证AnyBody建模系统(AMS)的下肢肌肉骨骼模型所预测的肌肉活动与正常行走任务的十名健康受试者的测得的肌肉活动(EMG)之间的关系。在这项研究中使用了AMS的GaitLowerExtremity模型(GLEM)。八个EMG电极测量了右腿的八块肌肉的活动:内侧大肌,外侧大肌,直肌,半腱肌,二头肌,腓肠肌和外侧肌以及胫骨前肌。在两条曲线上(预测和测量的肌肉活动)应用了四个不同的阈值:在以相同阈值水平进行定量比较之前,分别为图形阈值(MGT)平均值的10%,25%,35%和45%。肌肉活动的发作和偏移的数量用于量化一致性水平。目视检查表明,肌电图和预测的肌肉活动之间有很好的一致性。通常,对于所有参数,与其他MGT相比,45%的MGT表现出最好的一致性。对于发作和偏移的数量,两条肌肉显示出适度的一致,而四只肌肉显示出轻微的一致。从定量的角度来看,这是第一次尝试,结果显示,在8个变量中,有6个肌肉在发作和偏移的变量数上显示出轻微的正一致性。 AMS和EMG模式之间的差异可归因于建模过程的性质,例如假设和简化,以及运动过程中确定膝盖动力学数据的可能不准确。

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