Feature subset evaluation method for upper limb rehabilitation training based on joint feature discernibility

Guoyu Zuo,Jiahao Lu,Daoxiong Gong,Zhaokun Xu

doi:10.1177/1550147719838467

Abstract

A feature subset discernibility hybrid evaluation method using Fisher score based on joint feature and support vector machine is proposed for the feature selection problem of the upper limb rehabilitation training motion of Brunnstrom 4–5 stage patients. In this method, the joint feature is introduced to evaluate the discernibility between classes due to the joint effect of both candidate and selected features. A feature subset search strategy is used to search a set of candidate feature subsets. The Fisher score based on joint feature method is used to evaluate the candidate feature subsets and the best subset is selected as a new selected feature subset. From these selected subsets such as obtained by the above process, the subset with the best performance of support vector machine classification is finally selected as the optimal feature subset. Experiments were carried out on the upper limb routine rehabilitation training samples of the Brunnstrom 4–5 stage. Compared with both the F-score and the discernibility of feature subset methods, the experimental results show the effectiveness and feasibility of the proposed method which can obtain the feature subsets with higher accuracy and smaller feature dimension.

Highlights

With the increasing number of aging population in the world, the number of stroke patients is increasing; 85% of stroke patients have upper limb dysfunction in the early stage of the disease,[1] which seriously affects their quality of life.[2]
In order to verify the effectiveness of the FSJF-Support vector machine (SVM) feature subset discernibility hybrid evaluation method, we first combine the F-score and discernibility of feature subset (DFS) with SVM algorithm to form F-score-SVM and DFS-SVM hybrid evaluation method, and we combine the F-score-SVM, DFS-SVM, and FSJF-SVM with the sequential forward search (SFS) and the sequential backward search (SBS) feature search strategy, respectively, to form two groups of feature selection methods, and we carry out two groups of comparative experiments
In order to compare the performance of different feature subset evaluation methods more objectively, the SVM classifier used in the experiment uses the radial basis function (RBF) kernel, Figure 4

Summary

Introduction

With the increasing number of aging population in the world, the number of stroke patients is increasing; 85% of stroke patients have upper limb dysfunction in the early stage of the disease,[1] which seriously affects their quality of life.[2]. In the feature selection methods, the Filter method is popular among researchers, but most studies of feature subset which involve Filter method only consider the effect of individual candidate feature on the discernibility between classes, but ignore the impact of the joint effect of candidate features and selected features For this problem, this article introduces the distance between joint features into the framework of F-score method, and proposes a feature subset discernibility hybrid evaluation method by using Fisher score based on joint feature and support vector machine (FSJFSVM). This article introduces the distance between joint features into the framework of F-score method, and proposes a feature subset discernibility hybrid evaluation method by using Fisher score based on joint feature and support vector machine (FSJFSVM) In this method, FSJF is used as the evaluation criteria in Filter method, and SVM is used as the learning algorithm for evaluation in the Wrapper method. The hybrid feature evaluation method combining the two methods integrates the efficiency of the Filter method and the high accuracy of the Wrapper method, which can lead to obtaining a better feature subset

Related work

Experimental results and analyses

Conclusion