Boosting-Based EMG Patterns Classification Scheme for Robustness Enhancement

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Abstract
The high conventional accuracy of pattern recognition-based surface myoelectric classification in laboratory experiments does not necessarily result in high accessibility to practical protheses. An obvious reason is the effect of signals of untrained classes caused by the relatively small training dataset. In order to make the classifier robust to untrained classes, a classification scheme is developed based on boosting and random forest classifiers in this paper. Meanwhile, a threshold, the post probability of the prediction, is introduced as a balance (i.e., adjust) between the accurate classification and the rejection of the samples belonging to some untrained classes. The experiments are conducted to compare with other two schemes using linear discriminant analysis and support vector machines. Surface electromyogram signals, labeled with seven isometric movements, are collected from six healthy subjects' forearm. It is shown that the proposed scheme can reach up to about 92% accuracy in recognizing trained classes and 20% for untrained classes. Through adjusting the threshold, the accuracy of rejecting untrained classes reaches up to around 80%, with small decrease in recognizing trained classes (down to 80%). In the analysis of experiments' results, we also find that the proposed scheme has better error distribution among the classes.

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