基于DWT-VMD混合信号分解技术的人体活动识别  

Human Activity Recognition Based on DWT-VMD Hybrid Signal Decomposition

作　　者：陈金瑶 李瑞祥[1] 王星 施伟斌[1] CHEN Jinyao;LI Ruixiang;WANG Xing;SHI Weibin(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《数据采集与处理》2024年第3期736-749,共14页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(51705324)。

摘　　要：在人类活动识别的应用环境中,从原始传感器数据中提取更加有效的特征仍具有挑战性。针对该问题,利用离散小波变换(Discrete wavelet transform,DWT)和变分模式分解(Variational mode decomposition,VMD)的混合信号分解技术提取原始信号中的显著特征向量。在UCI-HAR数据集与SCUT-NAA数据集上,利用多种机器学习分类算法,例如K近邻、随机森林、LightGBM和XGBoost,对DWT-VMD混合信号分解算法的有效性进行了实验。实验结果表明,与未使用混合信号分解技术相比,使用该技术后识别准确率均有所提高,其中UCI-HAR数据集分类准确率达到98.91%,与未加入分解算法相比提高了1.79%;SCUT-NAA数据集分类准确率达到95.52%,提高了3.2%。在人体活动识别中,利用DWT-VMD混合信号分解技术,能够提取原始信号中更有效的特征,提高识别准确率,具有一定的实用性。In the application environment of human activity recognition,it is still challenging to extract sufficiently reliable features from the original sensor data.The hybrid signal decomposition technology of discrete wavelet transform(DWT)and variational mode decomposition(VMD)is used to extract the salient feature vectors from the original sensor signals to identify various human activities.Using a variety of machine learning classification algorithms,such as K-nearest neighbor,random forest,LightGBM and XGBoost,the effectiveness of the proposed algorithm is tested on UCI-HAR and SCUT-NAA data sets.Experimental results show that by using the hybrid signal decomposition technology,the recognition accuracy of all classification algorithms has been improved,with the maximum classification accuracy of 98.91% for UCI-HAR dataset,which has improved by 1.79% compared to not joining the decomposition algorithm.The maximum classification accuracy of SCUT-NAA dataset reaches 95.52%,which has improved by 3.2%.In human activity recognition,through the use of DWT-VMD hybrid signal decomposition technique,more effective features can be extracted from the original signal and the recognition accuracy can be further improved,showing the certain practical value of the technique.

关 键 词：人体活动识别 离散小波变换 变分模式分解 信号分解 机器学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]