Feature Subset Discernibility Evaluation Method for Upper Limb Rehabilitation Training Based on the Discernibility of Relative Distance

In this paper, a feature subset discernibility hybrid evaluation method based on the discernibility of relative distance and support vector machine (DRD-SVM) is proposed for the feature selection problem of the upper limb rehabilitation training motion of Brunnstrom 4-5 stage patients, in which a relative distance is introduced into evaluating the discernibility between classes considering the joint effect of both candidate and selected features. First, a feature subset search strategy is used to search a set of candidate feature subsets. Then the DRD is used to evaluate the candidate feature subsets, the best subset is selected as a new selected feature subset, and the feature subset with the best performance of SVM classification is selected as the optimal feature subset. Finally, feature selection experiment was carried out on upper limb routine rehabilitation training samples of the Brunnstrom 4-5 stage. The experimental results shows that, compared with the F-score method and the DFS one, the proposed method can obtain the feature subsets with higher accuracy and smaller feature dimension, which improves its effectiveness and feasibility.