Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach
Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach
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DOI：
10.1007/s00521-015-2089-3
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发表时间：
2017-03
影响因子：
6
通讯作者：
Vijay Bhaskar Semwal;K. Mondal;G. Nandi
中科院分区：
文献类型：
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作者：
Vijay Bhaskar Semwal;K. Mondal;G. Nandi
目前的工作描述了人类推恢复数据分类使用的功能，从固有模式函数通过执行经验模式分解不同的腿关节角度（髋关节，膝盖和脚踝）。计算了睁眼和闭眼受试者的关节角度数据。在实验过程中施加了四种推力（小，中，中等高，高），以分析恢复机制。使用深度神经网络（DNN）基于这些不同类型的推送进行分类，总体准确率达到89.28%。第一个分类器是基于前馈反向传播神经网络（FF-BPNN）的人工神经网络，第二个是基于DNN。所提出的基于DNN的分类器已经在四种类型的推送上应用和评估，即，小，中，中，高，高。使用五重交叉验证方法获得了88.4%的分类准确率。还进行了方差分析，以显示结果的统计显著性。一旦相应地识别了推动的类别（小、中、中等高、高），就可以利用相应的策略（髋、膝和踝）（Semwal等人，在International conference on control，automation，robotics and embedded systems（CARE），pp 1-6，2013）。
This current work describes human push recovery data classification using features that are obtained from intrinsic mode functions by performing empirical mode decomposition on different leg joint angles (hip, knee and ankle). Joint angle data were calculated for both open-eyes and closed-eyes subjects. Four kinds of pushes were applied (small, medium, moderately high, high) during the experiment to analyze the recovery mechanism. The classification was performed based on these different kinds of the pushes using deep neural network (DNN), and 89.28 % overall accuracy was achieved. The first classifier was based on artificial neural network on feed-forward back-propagation neural network (FF-BPNN), and second one was based on DNN. The proposed DNN-based classifier has been applied and evaluated on four types of pushes, i.e., small, medium, moderately high, high. The classification accuracy with a success of 88.4 % has been obtained using fivefold cross-validation approach. The analysis of variance has also been conducted to show the statistical significance of results. The corresponding strategies (hip, knee, and ankle) can be utilized once the categories of pushes (small, medium, moderately high, high) were identified accordingly push recovery (Semwal et al. in International conference on control, automation, robotics and embedded systems (CARE), pp 1–6, 2013).