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Research on Feature Importance of Gait Mechanomyography Signal Based on Random Forest

机译：基于随机林的步态力学信号信号的特征重要性研究

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In this paper, four tri-axis accelerometers are used to collect mechanomyography (MMG) signal of four thigh muscles during gait movements. Three time-domain features, two frequency-domain features, and 64 time-frequency domain features of the MMG were extracted, and the features were ranked by feature importance using random forest (RF). In addition, RF and a support vector machine (SVM) are utilized as classifiers, and the relationship between the number of features and the recognition accuracy is analyzed. After the feature importance analysis, the principal component analysis (PCA) was used to perform the dimensionality reduction, and the relationship between the reduced dimension and the accuracy was studied, moreover, RF and SVM were compared. The conclusion in the paper can help to select the features that have the greatest contribution to the gait recognition: zero-crossing rate, mean power frequency, median frequency, and energy coefficients of wavelet packet, providing theoretical basis for researchers to select suitable features in the future.
机译：在本文中，四个三轴加速度计用于在步态运动期间收集四条大腿肌肉的机械学（MMG）信号。提取了三个时间域特征，两个频域特征和MMG的64个时间域特征，并且通过随机森林（RF）进行特征重要性来排序。另外，RF和支持向量机（SVM）用作分类器，分析了特征数和识别精度之间的关系。在特征重要性分析之后，使用主成分分析（PCA）来进行维度降低，并且研究了减少维度与精度之间的关系，此外，比较RF和SVM。本文的结论可以帮助选择对步态识别最大贡献的特征：小波包的零交叉速率，均值功率，中值和能量系数，为研究人员选择合适的功能提供理论依据未来。

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