Al-Assaf Yousef
School of Engineering, The American University of Sharjah, Sharjah, United Arab Emirates.
IEEE Trans Biomed Eng. 2006 Nov;53(11):2248-56. doi: 10.1109/TBME.2006.883628.
Toward the goal of elbow and wrist prostheses control by characterizing events in surface myoelectric signals, this paper presents a dynamic method to simultaneously detect and classify such events. Dynamic cumulative sum of local generalized likelihood ratios using wavelet decomposition of the myoelectric signal is used for on-line detection. Frequency as well as energy changes are detected with this hybrid approach. Classification is composed of using multiresolution wavelet analysis and autoregressive modeling to extract signal features while polynomial classifiers are used for pattern modeling and matching. The results of detecting and classifying four elbow and wrist movements show that, in average, 91% of the events are correctly detected and classified using features obtained from multiresolution wavelet analysis while 95% accuracy is achieved with AR modeling. The classification accuracy decreases, however, if short prostheses response delay is desired. This paper also shows that the performance of the polynomial classifiers is better than that of the commonly used neural networks since it gives higher classification accuracy and consistent classification outcomes. In comparison to the well known support vector machine classification, the polynomial classifier gives similar results without the need to optimize and search for classifier parameters.
为了通过表征表面肌电信号中的事件来实现肘部和腕部假肢控制的目标，本文提出了一种动态方法，用于同时检测和分类此类事件。利用肌电信号的小波分解对局部广义似然比进行动态累积求和，用于在线检测。这种混合方法能够检测频率以及能量变化。分类包括使用多分辨率小波分析和自回归建模来提取信号特征，同时使用多项式分类器进行模式建模和匹配。对四种肘部和腕部运动进行检测和分类的结果表明，平均而言，使用多分辨率小波分析获得的特征能够正确检测和分类91%的事件，而通过自回归建模可实现95%的准确率。然而，如果需要短假肢响应延迟，分类准确率会降低。本文还表明，多项式分类器的性能优于常用的神经网络，因为它具有更高的分类准确率和一致的分类结果。与著名的支持向量机分类相比，多项式分类器无需优化和搜索分类器参数就能给出相似的结果。
