基于变分模态分解的冻结步态识别方法

Freezing of Gait Recognition Method Based on Variational Mode Decomposition

针对传统帕金森患者冻结步态识别方法自适应性不佳的问题,提出一种基于变分模态分解的冻结步态识别方法.首先采用变分模态分解代替传统时频分析方法对冻结步态信号进行充分的自适应分解.其次为提高算法识别精度和识别速度,选用CART模型作为集成分类器的基分类器并进行特征降维处理.最后针对不平衡数据集和单分类器性能有限的问题,进行了数据采样-集成分类器的设计并通过贝叶斯优化对识别算法进行超参数寻优.实验结果表明,相对于AdaBoost、Tomeklinks-AdaBoost和ROS-AdaBoost集成算法,RUSBoost集成算法可以更高效地完成冻结步态识别任务.

Aiming at the problem of poor self-adaptation of the traditional freezing of gait recognition method for Parkinson’s patients,the freezing of gait recognition method based on variational mode decomposition is proposed.Firstly,the variational mode decomposition is used instead of the traditional time-frequency analysis method to fully adaptively decompose the freezing of gait signal.Secondly,in order to improve the recognition accuracy and recognition speed of the algorithm,the CART model is selected as the base classifier of the ensemble classifier and the feature dimension reduction process is performed.Finally,aiming at the problem of unbalanced data set and limited performance of single classifier,the data sampling-ensemble classifier is designed and the recognition algorithm is optimized by Bayesian optimization.The experimental results show that compared with Adaboost,Tomeklinks-Adaboost,and ROS-Adaboost ensemble algorithm,RUSBoost ensemble algorithm can complete the freezing of gait recognition task more efficiently.