It is of great importance to effectively measure gait features and recognize the signature gait patterns for gait rehabilitation. In this work, we used a skeleton point detection to extract gait features and proposed an improved fuzzy decision to select the most significant features for classifying gait patterns. Thirteen gait recognition features were extracted from the obtained skeleton points data. Taking the extracted features as an input, our improved fuzzy similarity priority decision method has obtained important sequences of all features based on the relatively important scores. Then, the ranked features were delivered in different classifiers by a sequential forward selection strategy to select the optimal feature subset. There were significant differences between groups in each of the thirteen gait recognition features (p < 0.005), indicating that all extracted features are potential influence factors for classifying gait patterns. We also found that the highest classification accuracy of 100% for gait feature subsets included the stride frequency, maximum flexion angle of knee, and toe-out angle, on the all classifiers. The results suggest that the proposed approaches are very useful in searching for the optimal feature subset in present dataset.

有效地测量步态特征并识别步态康复的标志性步态模式非常重要。在这项工作中，我们使用骨架点检测来提取步态特征，并提出了一种改进的模糊决策来选择最重要的特征来分类步态模式。从获得的骨骼点数据中提取十三个步态识别特征。以提取的特征为输入，我们改进的模糊相似度优先决策方法根据相对重要的分数获得所有特征的重要序列。然后，通过顺序前向选择策略将排序后的特征传递到不同的分类器中，以选择最佳特征子集。组间在十三个步态识别特征中的每一个都存在显着差异（p  < 0.005），表明所有提取的特征都是对步态模式进行分类的潜在影响因素。我们还发现步态特征子集的最高分类准确率为 100%，包括所有分类器的步频、膝关节最大屈曲角度和脚趾外展角度。结果表明，所提出的方法对于在当前数据集中搜索最佳特征子集非常有用。