Hand motion recognition via multi-kernel manifold learning

Abstract

In the context of smart city, hand motion recognition has once again become a research hotspot, especially the surface electromyographic (sEMG) based hand motion recognition. When sEMG signals contain noise, the overfitting problem always exists. To solve this problem, a multi-kernel label relaxation ridge regression with manifold embedding is proposed. Firstly, a non-negative matrix is introduced to relax the label matrix of the training data, so that the proposed model has more freedom in label fitting. At the same time, label relaxation can enlarge the margins between classes as much as possible. Secondly, based on the idea of manifold learning, the compactness graph is introduced to ensure that the training data in the same class should be as close as possible in the conversion space when they are converted from the original feature space to the label space so as to improve the generalization ability. Finally, 15 UCI datasets and the hand motion dataset Ninapro are used to verify the proposed model. The experimental results show that the proposed model has certain advantages over comparative ones in terms of classification accuracy and generalization ability.