基于结构优化的DDAG-SVM上肢康复训练动作识别方法

针对上肢康复训练系统中训练评估方法核心的动作识别问题,提出一种面向Brunnstrom 4~5期患者上肢康复训练动作的SODDAG-SVM(Structure-optimized decision directed acyclic graph-support vector machine)多分类识别方法.首先将多分类问题分解成一组二分类问题,并使用支持向量机构建各二分类器,分别采用遗传算法和特征子集区分度准则对各二分类器的核函数参数及特征子集进行优化.然后使用类对的SVM二分类器泛化误差来衡量每个类对的易被分离程度,并由其建立类对泛化误差上三角矩阵.最后由根节点开始,依次根据各节点的泛化误差矩阵,通过选择其中最易被分离类对的SVM分类器构成该节点的方式,来构建SODDAG-SVM多分类器结构.当待预测的实例较少时,直接构建实例经过的SODDAG-SVM部分结构并对实例进行预测;当待预测的实例较多时,先构建完整的SODDAG-SVM结构,再代入所有实例进行预测.通过人体传感技术获得Brunnstrom 4~5阶段上肢康复训练的常用动作样本集,进行SODDAG-SVM动作识别实验,准确率达到了95.49%,结果均优于常规的决策有向无环图(Decision directed acyceic graph,DDAG)和MaxWins方法,实验表明本文方法能有效地提高上肢康复训练动作识别的准确率.

A Structure-optimized DDAG-SVM Action Recognition Method for Upper Limb Rehabilitation Training

An SODDAG-SVM (structure-optimized decision directed acyclic graph-support vector machine) multi-classification action recognition method of upper limb rehabilitation training for the Brunnstrom 4~5 stage patients is proposed to solve the core problem of action recognition of the rehabilitation training evaluation method. First, the multi-classification problem is decomposed into a set of binary classification problems, support vector machine (SVM) method is used to construct each binary classifier, in which the SVM kernel function parameters and feature subsets of each binary classifiers are optimized by genetic algorithm and the feature subsets discrimination criterion, respectively. Then, the generalization errors of each SVM binary classifier are used to measure the separable degree of this class pair, and the upper triangulation matrix of generalization errors is built. Finally, from the root node, according to the generalization error matrix of each node, an SODDAG-SVM structure is constructed by choosing the SVM classifier of the most easily separated class pair as each node. When there are fewer instances to be predicted, a part of the SODDAG-SVM structure passed by these instances is directly built for predicting the instances. When more instances need to be predicted, a complete SODDAG-SVM structure is first constructed and then is used to predict all the instances. Action recognition experiment is performed on the upper limb routine rehabilitation training samples of the Brunnstrom 4~5 stage, acquired using human body sensing technology. Results show that the accuracy reaches 95.49% which is higher than those of conventional decision directed acyceic graph (DDAG) and MaxWins methods. It is proved that the proposed method can effectively improve the accuracy of rehabilitation training action recognition.