摘要
针对从原始加速度数据中提取肌音信号以及实现基于肌音信号对6种常见人体下肢活动的分类问题,提出了一种基于特征模态分解算法的肌音信号滤波降噪方法,以及一种肌音信号时频域特征提取方法和基于核主成分分析的特征集降维方法,还有一种基于融合注意力机制的时域卷积网络下肢活动分类方法.分析显示,通过特征模态分解算法得到的肌音信号的包络熵值最小,仅为8.13,这表明特征模态分解能够高效地提取肌音信号并去除随机噪声.此外,通过特征模态分解算法得到的肌音信号的功率谱密度与原始信号相比,在运动伪迹所在低频频段下降明显,在肌音信号所在频段下降最少,这表明特征模态分解能够高效去除伪迹干扰,同时保留最多的肌音信号数据.针对基于肌音信号对6种常见人体下肢活动的分类问题,采用一种全面的特征提取策略,从16路肌音信号中提取了448种特征,并通过核主成分分析对特征集进行降维处理,还构建了一个融合注意力机制的时域卷积网络,以实现对分类模型的训练.另外,通过应用正-余弦北方苍鹰优化算法对网络进行超参数优化,分析显示所提出的模型在分类准确度方面表现出色,达到了98.4%.
Aiming at the problem of extracting mechanomyography(MMG)signals from raw acceleration data and classifying the six common lower limb activities based on MMG signals,a MMG signal filtering and denoising method based on the feature mode decomposition(FMD)algorithm,a time-frequency domain feature extraction method for MMG signals,a feature set dimensionality reduction method based on kernel principal component analysis(KPCA),and a lower limb activity classification method based on a temporal convolutional network(TCN)incorporating an attention mechanism were proposed.Analysis shows that the envelope entropy value of the MMG signal obtained through the FMD algorithm was the smallest,only 8.13,indicating that FMD can efficiently extract MMG signals and remove random noise.Additionally,compared to the original signal,the power spectral density of the MMG signal obtained via the FMD algorithm showed a significant reduction in the lowfrequency band where motion artifacts resided,while the reduction was minimal in the frequency band where MMG signals were located.This demonstrates that FMD effectively removed artifact interference while retaining the majority of MMG signal data.For the classification of the six common lower limb activities based on MMG signals,a comprehensive feature extraction strategy was adopted,extracting 448 features from 16-channel MMG signals and reducing the feature set dimensionality through KPCA.A TCN incorporating an attention mechanism was also constructed to train the classification model.Furthermore,by applying the sine-cosine northern goshawk optimization(SCNGO)algorithm to optimize the network’s hyperparameters,analysis reveals that the proposed model achieves outstanding classification accuracy,reaching 98.4%.
作者
白宇
管小荣
王铮
张睿
程实
李回滨
BAI Yu;GUAN Xiaorong;WANG Zheng;ZHANG Rui;CHENG Shi;LI Huibin(School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing,Jiangsu 210094,China;Zhiyuan Research Institute,Hangzhou,Zhejiang 310000,China)
出处
《北京理工大学学报》
北大核心
2025年第12期1312-1322,共11页
Transactions of Beijing Institute of Technology
基金
智元实验室开放基金项目(ZYL2024017a)
江苏省研究生创新项目资助(KYCX23-0512)。
关键词
信号处理
肌音信号
特征模态分解
注意力机制
时域卷积模型
人体运动分类
signal processing
mechanomyography
feature mode decomposition
attention mechanism
tempor-al convolutional network
classification of human activity
