基于改进卷积神经网络的动力下肢假肢运动意图识别

Intent recognition of power lower-limb prosthesis based on improved convolutional neural network

传统动力下肢假肢运动意图识别算法常使用机器学习算法分类器,在特征选择方面则需要手工提取.针对该问题将深度学习算法应用于运动意图识别研究中,通过在传统的卷积神经网络的基础上进行改进,使算法更适应于基于短时行为样本数据的运动意图识别,同时抑制深度学习算法应用于运动意图识别中的过拟合.在意图识别数据集中进行滑动窗口预处理,目的是对时间序列样本做数据增广,扩增目标数据集能够使训练集更加丰富全面,提高识别的精度,运用改进后的卷积神经网络对增广后的数据集进行特征学习与分类.实验结果表明,该方法在13类运动模式下的识别率达到93%.

Traditional intent recognition algorithms of power lower-limb prosthesis often use machine learning algorithm classifiers,which require manual extraction in feature selection.In this paper,using deep learning algorithms in motion intent recognition research,the algorithm is improved on the basis of the traditional convolutional neural network,and is more suitable for the motion intent recognition based on the short-term behavior sample data,while suppressing the application of deep learning algorithms in motion intent recognition.The sliding window preprocessing is performed on the intent recognition data set.The purpose is to augment the data of the time series samples.Amplifying the target data set can make the training set more abundant and comprehensive,improve the accuracy of recognition.The improved convolutional neural network is used to perform feature learning and classification on the augmented data set.The experimental results show that the recognition rate of the improved algorithm under the 13 types of motion patterns reaches 93%.