Samuel Oluwarotimi Williams, Asogbon Mojisola Grace, Geng Yanjuan, Chen Shixiong, Feng Pang, Chuang Lin, Wang Lin, Li Guanglin
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:3513-3516. doi: 10.1109/EMBC.2018.8513015.
Electromyogram pattern recognition (EMG-PR) based control is a potential method capable of providing intuitively dexterous control functions in upper limb prostheses. Meanwhile, the feature extraction method adopted in EMG-PR based control is considered as an important factor that influences the performance of the prostheses. By exploiting the limitations of the existing feature extraction methods, this study proposed a new feature extraction method to effectively characterize EMG signal patterns associated with different limb movement intent. The performance of the proposed 2-dimensional novel time-domain feature set (NTDFS) was investigated using classification accuracy and feature space separability metrics across five subjects' EMG recordings, and compared with four different existing methods. In comparison to four other previously proposed feature extraction methods, the NTDFS achieved significantly better performance with increment in accuracy in the range of 5.20% ∼ 8.40% at p<0.05. Additionally, by applying principal component analysis (PCA) technique, the PCA feature space for NTDFS show obvious class separability in comparison to the other existing feature extraction methods. Thus, the proposed NTDFS may facilitate the development of accurate and robust clinically viable EMG-PR based prostheses.
基于肌电图模式识别（EMG-PR）的控制是一种有潜力的方法，能够在上肢假肢中提供直观灵活的控制功能。同时，基于EMG-PR的控制中采用的特征提取方法被认为是影响假肢性能的一个重要因素。通过利用现有特征提取方法的局限性，本研究提出了一种新的特征提取方法，以有效地表征与不同肢体运动意图相关的肌电信号模式。使用分类准确率和特征空间可分离性指标，在五个受试者的肌电记录上研究了所提出的二维新颖时域特征集（NTDFS）的性能，并与四种不同的现有方法进行了比较。与其他四种先前提出的特征提取方法相比，NTDFS在p<0.05时，准确率提高了5.20%至8.40%，表现出显著更好的性能。此外，通过应用主成分分析（PCA）技术，与其他现有特征提取方法相比，NTDFS的PCA特征空间显示出明显的类可分离性。因此，所提出的NTDFS可能有助于开发准确、可靠且临床上可行的基于EMG-PR的假肢。
