By Topic

Recognition of Electromyographic Signals Using Cascaded Kernel Learning Machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yi-Hung Liu ; Chung Yuan Christian Univ., Chungli ; Han-Pang Huang ; Chang-Hsin Weng

Electromyographic (EMG) signals recognition is a complex pattern recognition problem due to its property of large variations in signals and features. This paper proposes a novel EMG classifier called cascaded kernel learning machine (CKLM) to achieve the goal of high-accuracy EMG recognition. First, the EMG signals are acquired by three surface electrodes placed on three different muscles. Second, EMG features are extracted by autoregressive model (ARM) and EMG histogram. After the feature extraction, the CKLM is performed to classify the features. CKLM is composed of two different kinds of kernel learning machines: generalized discriminant analysis (GDA) algorithm and support vector machine (SVM). By using GDA, both the goals of the dimensionality reduction of input features and the selection of discriminating features, named kernel FisherEMG, can be reached. Then, SVM combined with one-against-one strategy is executed to classify the kernel FisherEMG. By cascading SVM with GDA, the input features will be nonlinearly mapped twice by radial-basis function (RBF). As a result, a linear optimal separating hyperplane can be found with the largest margin of separation between each pair of postures' classes in the implicit dot product feature space. In addition, we develop a digital signal processor (DSP)-based EMG classification system for the control of a multi-degrees-of-freedom prosthetic hand for the practical implementation. Based on the clinical experiments, the results show that the proposed CKLM is superior to other frequently used methods, such as k-nearest neighbor algorithm, multilayer neural network, and SVM. The best EMG recognition rate 93.54% is obtained by CKLM.

Published in:

IEEE/ASME Transactions on Mechatronics  (Volume:12 ,  Issue: 3 )