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Quantitative relationship between feature extraction of sEMG and upper limb elbow joint angle

机译：SEMG和上肢弯头关节角度特征提取的定量关系

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Feature extraction is an important part in the classifier systems. In this study, feature extraction was used to extract the information of the surface electromyography (sEMG) and to predict upper limb elbow joint angle. To predict the upper limb elbow joint angle, we explored the EMG signal characteristics on biceps, triceps lateral head and triceps long head. Time domain of feature extraction is still the best feature extraction to get the information on signal in a real time processing. Feature extraction, RMS, MAV, I EMG, ZC, VAR, and SSC are commonly used by researchers to extract feature in sEMG. The quantification of the relationship between feature extraction and elbow joint angle was measured using the root mean square error (RMSE) and Pearson Correlation Coefficient (CC). In this research, we found that the feature extraction ZC was the best feature extraction in time domain to predict the elbow joint angle, with normalized RMSE 0.187o and CC 0.907. With these findings, it can facilitate the researcher in classifier step to build exoskeleton based EMG control.
机译：特征提取是分类器系统中的重要组成部分。在该研究中，使用特征提取来提取表面肌电图（SEMG）的信息并预测上肢弯头关节角度。为了预测上肢弯头关节角度，我们探讨了二头肌的EMG信号特性，肱三头肌侧向头和三头肌长头。特征提取的时域仍然是最好的特征提取，以在实时处理中获取有关信号的信息。特征提取，RMS，MAV，I EMG，ZC，VAR和SSC通常由研究人员使用SEMG提取功能。使用根均方误差（RMSE）和Pearson相关系数（CC）测量特征提取和肘关节角之间的关系的量化。在本研究中，我们发现特征提取ZC是时域中的最佳特征提取，以预测弯头关节角度，归一化RmSE 0.187O和CC 0.907。通过这些发现，它可以促进分类器步骤的研究人员构建基于外骨骼的EMG控制。

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