Electronic & Computer Science School, University of Shouthampton, Southampton SO17 1BJ, UK.
Electronic Information School, Wuhan University, Wuhan 430072, China.
Sensors (Basel). 2022 Feb 18;22(4):1604. doi: 10.3390/s22041604.
Bioinformation is information generated from biological movement. By using a variety of modern technologies, we can use this information to form a meaningful model for researchers to study. An electromyographic (EMG) signal is one type of bioinformation that is used in many areas to help people study human muscle movement. This information can help in both clinical areas and industrial areas. EMG is a very complicated signal, so processing it is vital. The processing of EMG signals is divided into collection, denoising, decomposition, feature extraction and classification steps. In this article, the wavelet denoising step and several decomposition processes are discussed to show the usage of this technique in the final classification step. At the end of the study, we find that after the wavelet denoising step, the classification accuracy, which uses the K-nearest neighbor of the independent component analysis features, improves, but the accuracy of the wavelet coefficient features and autoregression coefficient features decreases.
生物信息是由生物运动产生的信息。通过使用各种现代技术，我们可以利用这些信息为研究人员研究形成有意义的模型。肌电图（EMG）信号是一种生物信息，在许多领域被用于帮助人们研究人体肌肉运动。该信息在临床领域和工业领域都有重要的作用。EMG 是一种非常复杂的信号，因此对其进行处理至关重要。EMG 信号的处理分为采集、去噪、分解、特征提取和分类步骤。在本文中，讨论了小波去噪步骤和几种分解过程，以展示该技术在最终分类步骤中的应用。在研究结束时，我们发现经过小波去噪步骤后，使用独立成分分析特征的 K-最近邻的分类准确率提高了，但小波系数特征和自回归系数特征的准确率降低了。
