An incremental EMG classification model to detect and recognize randomly-occurred outlier motion

摘要

Traditional EMG-motion recognition methods are only able to recognize target motions that presented in the training phase, but cannot detect randomly-occurred outlier motions that did not present before. Here, a hybrid classifier that combines one-class SVMs and a multi-class LDA was proposed to perform recognition on target classes and rejection on outlier classes. The classification ability of the hybrid classifier can incrementally grow via online learning the data of outlier classes. Extensive experiments on EMG-based hand-motion recognition were conducted to verify the performance of the incremental hybrid classifier (IHC). The mean recognition accuracy on target classes of IHC is 92%, which is 23% higher than that of the normal MLP. Moreover, IHC has the ability to detect outlier patterns that MLP would misclassify to target classes.