Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Web Browser Control Using EMG Based Sub Vocal Speech Recognition
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 09
Twin Support Vector Machines for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Understanding hand gestures using approximate graph matching
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Online hand gesture recognition using surface electromyography based on flexible neural trees
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Probabilistic outputs for twin support vector machines
Knowledge-Based Systems
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Surface electromyogram (sEMG) is a measure of the muscle activity from the skin surface, and is an excellent indicator of the strength of muscle contraction. It is an obvious choice for control of prostheses and identification of body gestures. Using sEMG to identify posture and actions that are a result of overlapping multiple active muscles is rendered difficult by interference between different muscle activities. In the literature, attempts have been made to apply independent component analysis to separate sEMG into components corresponding to the activities of different muscles, but this has not been very successful, because some muscles are larger and more active than the others. We address the problem of how to learn to separate each gesture or activity from all others. Multicategory classification problems are usually solved by solving many one-versus-rest binary classification tasks. These subtasks naturally involve unbalanced datasets. Therefore, we require a learning methodology that can take into account unbalanced datasets, as well as large variations in the distributions of patterns corresponding to different classes. This paper reports the use of twin support vector machine for gesture classification based on sEMG, and shows that this technique is eminently suited to such applications.