Al-Assaf Y, Al-Nashash H
School of Engineering, University of Sharjah, Sharjah, UAE.
J Med Eng Technol. 2005 Sep-Oct;29(5):203-7. doi: 10.1080/03091900412331289906.
This paper represents an ongoing investigation for surface myoelectric signal segmentation and classification. The classical moving average technique augmented with principal components analysis and time-frency analysis were used for segmentation. Multiresolution wavelet analysis was adopted as an effective feature extraction technique while artificial neural networks were used for classification. Results of classifying four elbow and wrist movement signals recorded from biceps and triceps gave 5.1% classification error when two channels were used.
本文介绍了一项关于表面肌电信号分割与分类的正在进行的研究。采用结合主成分分析和时频分析的经典移动平均技术进行分割。采用多分辨率小波分析作为有效的特征提取技术，同时使用人工神经网络进行分类。对从肱二头肌和肱三头肌记录的四种肘部和腕部运动信号进行分类的结果表明，当使用两个通道时，分类误差为5.1%。
