A Structure-optimized DDAG-SVM Action Recognition Method for Upper Limb Rehabilitation Training
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摘要: 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.
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关键词:
- 上肢康复训练 /
- 动作识别 /
- SODDAG-SVM /
- 多分类器 /
- 二分类器
Abstract: 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.-
Key words:
- Upper limb rehabilitation training /
- action recognition /
- structure-optimized decision directed acyclic graph-support vector machine (SODDAG-SVM) /
- multi-class classifier /
- binary classifier
1) 本文责任编委 白翔 -
图 7 特征选择对于SVM二分类器性能的影响
Fig. 7 Effect of feature selection on the accuracy of SVM binary classifier
表 1 $C$, $g$参数寻优结果
Table 1 Optimization results of the parameters of $C$, $g$
类对 最优$C$ 最优$g$ 最优准确率(%) 1v2 0.009793572 0.0022394 100 1v3 0.33971 1.08$\times 10^6$ 100 1v4 1 9.73$\times 10^6$ 95.5517 1v5 0.613049119 0.001941097 89.6552 1v6 1.10$\times 10^6$ 0.00721982 95.4371 2v3 1 0.000019687 88.6836 2v4 0.023029107 0.019702158 83.8836 2v5 0.967574582 0.003192183 100 2v6 1 7.00$\times 10^13$ 100 3v4 0.5 0.000033106 90 3v5 0.25 6.03$\times 10^6$ 96.6689 3v6 1 0.000047963 86.6545 4v5 0.26493253 0.008829793 83.3582 4v6 0.825159853 0.020969172 88.6883 5v6 3.1249 0.000052136 86.6667 
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表 2 特征子集选择结果
Table 2 Feature subset selection results
类对 最优特征子集 测试集准确率(%) 1v2 全部特征 100 1v3 全部特征 100 1v4 16, 1, 38, 25, 29, 7, 11, 20, 33, 50, 17, 30, 47 100 1v5 26, 39, 1 95.4767 1v6 26, 37 95 2v3 37, 24, 46 100 2v4 16, 29, 47, 37, 24, 21 100 2v5 21, 16, 29, 47, 34, 50, 37, 24, 46, 39, 26, 43, 13, 1, 52, 25, 32, 38, 45, 19, 4, 11, 41, 20, 28, 15, 42 100 2v6 39, 37, 24, 47, 50, 26, 21 100 3v4 37, 46, 42 94.6536 3v5 27, 14, 46, 42, 39, 26, 21, 17, 41, 13, 51, 30, 50, 16, 8, 7, 32, 19, 45 95.3467 3v6 13, 39, 26, 46, 17, 30, 42 100 4v5 29, 16, 39, 26, 19 100 4v6 39, 16, 26, 13, 29, 24 100 5v6 16 100
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表 3 分类准确率对比(%)
Table 3 Comparison of classification accuracy(%)
DDAG SODDAG MaxWins 平均值 92.09 95.49 91.04 最大值 96.89 / / 最小值 89.83 / /
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表 4 时间开销对比(s)
Table 4 Comparison of time cost (s)
DDAG SODDAG MaxWins 时间 0.7459 1.0167 1.5723
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表 5 SODDAG-SVM混淆矩阵
Table 5 SODDAG-SVM confusion matrix
类别 1 2 3 4 5 6 1 180 0 0 0 12 8 2 0 200 0 0 0 0 3 0 0 182 9 9 0 4 0 0 6 194 0 0 5 3 0 4 0 193 0 6 3 0 0 0 0 197
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