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首页> 外文会议>National Conference on Biomedical Engineering;International Iranian Conference on Biomedical Engineering >Continuous Estimation of Knee Joint Angle during Squat from sEMG using Artificial Neural Networks
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Continuous Estimation of Knee Joint Angle during Squat from sEMG using Artificial Neural Networks

机译：使用人工神经网络从SEMG蹲下膝关节角度的连续估计

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The purpose of this research was to continuous knee joint angle estimation from sEMG during squat using artificial neural networks. sEMG signals of vastus medialis, rectus femoris, biceps femoris and 3D kinematics of lower extremity joints for four participants during squat were captured at 1500 Hz and 100 Hz, respectively. sEMG signals were preprocessed and RMS and variance were extracted as input features. The processed input data was given to a three-layer feed forward neural network with one hidden layer. The proposed network was trained by the Levenberg-Marquardt algorithm. The root mean square error (RMSE) and correlation coefficient (CC) were used to evaluate the accuracy of estimation. The results showed that this network is able to continuously estimate the knee joint angle with global RMSE of 5.0041° ± 0.9963° and CC of 0.9898 ± 0.0039. It concludes that a multilayer neural network with a simple structure has the ability to continuously estimate the joint angle from sEMG data while performing an athletic movement under real loading situation.
机译：该研究的目的是使用人工神经网络在SEMOG期间连续膝关节角估计。在蹲下期间，四个参与者的下肢关节的SEMG信号分别为1500Hz和100Hz。分别捕获了四个参与者的下肢关节的矩阵，二头肌股骨和3D运动学。 SEMG信号被预处理，并提取RMS和方差作为输入特征。处理后的输入数据给出了一个带有一个隐藏层的三层馈送前进神经网络。所提出的网络受到Levenberg-Marquardt算法的培训。均均方误差（RMSE）和相关系数（CC）用于评估估计的准确性。结果表明，该网络能够连续估计膝关节角，全球RMSE为5.0041°±0.9963°，CC为0.9898±0.0039。结论是，具有简单结构的多层神经网络具有能够连续估计SEMG数据的关节角度，同时在真正的装载情况下进行运动运动。

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