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首页> 外文会议>Iranian Conference on Biomedical Engineering >A PCA-assisted EMG-driven model to predict upper extremities' joint torque in dynamic movements
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A PCA-assisted EMG-driven model to predict upper extremities' joint torque in dynamic movements

机译：PCA辅助EMG驱动模型，以预测动态运动中的上肢关节扭矩

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To relate electromyographic signals (EMG) to net joint torque, different approaches have been taken into account. In this regard, some researchers chose to use Principal Component Analysis (PCA). A Study in 2001 reported a linear relationship between the PCA-processed EMG data and the joint torque while investigating isometric movements. In this project we questioned the possibility to use this method for free dynamic tasks. Four healthy subjects participated in the current study, performing three sets of Dumbbell Kick Back exercise for five different dumbbell weights. The net joint torque was calculated using the kinematic data in an inverse dynamics model. Meanwhile the EMG data were processed with a PCA method, and then were input to the model to estimate the joint torque. In order to predict this torque, we used two models; a single-input model that was fed with the PCA-processed EMG of the all corresponding muscles; and a double-input model that utilized the PCA-processed EMG data of the agonist and antagonist muscles separately. The results demonstrated that both the single-input and double-input models are capable of predicting the torque for both isometric and free dynamic tasks. Employing a paired t-test we found that the double-input model was significantly more successful in estimating the torque comparing to the single-input model (p < 0.005). The other factor (the movement type) proved to also have a significant effect on the estimation outcome (p < 0.0005). In general, this study suggests that a linear relationship exists between PCA-processed EMG data and the joint torque in both isometric and free dynamic movements; however, in order to have a better estimate of the net joint torque, distinguishing the agonist-antagonist muscle groups' generated torques may be beneficial.
机译：为了将电拍摄信号（EMG）联系到净联合扭矩，已经考虑了不同的方法。在这方面，一些研究人员选择使用主成分分析（PCA）。 2001年的一项研究报告了PCA处理的EMG数据与关节扭矩之间的线性关系，同时调查了等距运动。在这个项目中，我们质疑可能使用此方法进行免费动态任务。四个健康的科目参与了当前的研究，执行三套哑铃踢回锻炼，为五种不同的哑铃重量。使用逆动力学模型中的运动数据计算净联合扭矩。同时，使用PCA方法处理EMG数据，然后输入到模型以估计关节扭矩。为了预测这种扭矩，我们使用了两种型号;用所有相应肌肉的PCA处理的肌肉加入单个输入模型;和一个双输入模型，分别利用激动剂和拮抗剂肌肉的PCA处理的EMG数据。结果表明，单输入和双输入模型都能够预测等距和自由动态任务的扭矩。采用配对T检验我们发现双输入模型在估计与单输入模型的扭矩（P <0.005）估计扭矩方面更为成功。另一种因素（运动型）证明对估计结果产生显着影响（P <0.0005）。通常，该研究表明，在PCA处理的EMG数据和等距和自由动态运动中的关节扭矩之间存在线性关系;然而，为了更好地估计净联合扭矩，区分激动剂 - 拮抗剂肌肉群的产生扭矩可能是有益的。

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