A wavelet-based continuous classification scheme for multifunction myoelectric control Export

@article{citeulike:3795915, abstract = {This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity.}, author = {Englehart, K. and Hudgin, B. and Parker, P. A.}, booktitle = {IEEE Transactions on Biomedical Engineering}, citeulike-article-id = {3795915}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/10.914793}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=914793}, citeulike-linkout-2 = {http://dx.doi.org/http://dx.doi.org/10.1109/10.914793}, citeulike-linkout-3 = {http://dx.doi.org/10.1109/10.914793}, doi = {10.1109/10.914793}, journal = {IEEE Transactions on Biomedical Engineering}, number = {3}, pages = {302--311}, posted-at = {2008-12-16 09:44:53}, priority = {2}, title = {A wavelet-based continuous classification scheme for multifunction myoelectric control}, url = {http://dx.doi.org/10.1109/10.914793}, volume = {48}, year = {2001} }

TY - JOUR ID - citeulike:3795915 L3 - citeulike-article-id:3795915 N2 - This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in previous work. Fundamental to the success of this method is the use of a wavelet-based feature set, reduced in dimension by principal components analysis. Further, it is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to one or two channels. It is demonstrated that exceptionally accurate performance is possible using the steady-state myoelectric signal. Exploiting these successes, a robust online classifier is constructed, which produces class decisions on a continuous stream of data. Although in its preliminary stages of development, this scheme promises a more natural and efficient means of myoelectric control than one based on discrete, transient bursts of activity. IS - 3 JF - IEEE Transactions on Biomedical Engineering EP - 311 TI - A wavelet-based continuous classification scheme for multifunction myoelectric control VL - 48 T2 - IEEE Transactions on Biomedical Engineering SP - 302 AU - Englehart, K AU - Hudgin, B AU - Parker, PA PY - 2001/// UR - http://dx.doi.org/10.1109/10.914793 ER -